{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c925679-fe32-47c0-bf55-56d3b4b35adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "from patsy import builtins as pb\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f272de2-9388-4efd-a76c-b52ebfdaf474",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR  = r\"E:\\\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3de618-957c-44f0-be4a-0fc1761eeba5",
   "metadata": {},
   "source": [
    "## 1. Table 2 FUll results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "761f73d8-727e-4da9-9c49-1a9a5512a3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Full regression results (Models 1–4)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Model 1\n",
       "N = 3,000 | R² = 0.160 | Adj. R² = 0.151"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.969</td>\n",
       "      <td>0.074</td>\n",
       "      <td>53.719</td>\n",
       "      <td>0</td>\n",
       "      <td>***</td>\n",
       "      <td>3.824</td>\n",
       "      <td>4.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.343</td>\n",
       "      <td>0.732</td>\n",
       "      <td></td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/Prefer not to say]</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.198</td>\n",
       "      <td>0.843</td>\n",
       "      <td></td>\n",
       "      <td>-0.141</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-1.176</td>\n",
       "      <td>0.24</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.052</td>\n",
       "      <td>0.293</td>\n",
       "      <td></td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.799</td>\n",
       "      <td></td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (enrolled)]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.767</td>\n",
       "      <td></td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>0.508</td>\n",
       "      <td></td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.997</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.163</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.929</td>\n",
       "      <td>0.353</td>\n",
       "      <td></td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.539</td>\n",
       "      <td>0.59</td>\n",
       "      <td></td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.257</td>\n",
       "      <td>0.024</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.213</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.261</td>\n",
       "      <td>0.794</td>\n",
       "      <td></td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.996</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.932</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Not married]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.508</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(marital_status)[T.Others (Divorced/Widowed...</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.612</td>\n",
       "      <td></td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_status)[T.Not in labor force]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.684</td>\n",
       "      <td></td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_status)[T.Other]</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-1.205</td>\n",
       "      <td>0.228</td>\n",
       "      <td></td>\n",
       "      <td>-0.297</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_status)[T.Permanent]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.887</td>\n",
       "      <td></td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment_status)[T.Self-employed]</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.041</td>\n",
       "      <td>1.398</td>\n",
       "      <td>0.162</td>\n",
       "      <td></td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(employment_status)[T.Unemployed/seeking]</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.997</td>\n",
       "      <td></td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation_group)[T.Other/NA]</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.683</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation_group)[T.Production/On-site]</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.356</td>\n",
       "      <td>0.175</td>\n",
       "      <td></td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation_group)[T.Professional/Managerial]</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-1.632</td>\n",
       "      <td>0.103</td>\n",
       "      <td></td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation_group)[T.Public sector]</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.384</td>\n",
       "      <td></td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAT(occupation_group)[T.Service/Sales]</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-1.266</td>\n",
       "      <td>0.205</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAT(union_membership)[T.Not applicable]</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.680</td>\n",
       "      <td>0.496</td>\n",
       "      <td></td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAT(union_membership)[T.Yes]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.745</td>\n",
       "      <td></td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>exposure</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.022</td>\n",
       "      <td>15.856</td>\n",
       "      <td>1.28e-56</td>\n",
       "      <td>***</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>beneficiary</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.022</td>\n",
       "      <td>16.630</td>\n",
       "      <td>4.23e-62</td>\n",
       "      <td>***</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>0.681</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-1.716</td>\n",
       "      <td>0.0862</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.969   0.074  53.719   \n",
       "1                                 CAT(gender)[T.Male] -0.007   0.022  -0.343   \n",
       "2              CAT(gender)[T.Other/Prefer not to say] -0.013   0.065  -0.198   \n",
       "3                   CAT(region)[T.Non-capital region] -0.026   0.022  -1.176   \n",
       "4                         CAT(education)[T.Graduate+]  0.040   0.038   1.052   \n",
       "5               CAT(education)[T.High school or less] -0.007   0.029  -0.254   \n",
       "6           CAT(education)[T.Some college (enrolled)]  0.008   0.027   0.296   \n",
       "7   CAT(monthly_household_income_band_million_krw)... -0.028   0.042  -0.662   \n",
       "8   CAT(monthly_household_income_band_million_krw)... -0.082   0.041  -1.997   \n",
       "9   CAT(monthly_household_income_band_million_krw)... -0.040   0.043  -0.929   \n",
       "10  CAT(monthly_household_income_band_million_krw)... -0.024   0.045  -0.539   \n",
       "11  CAT(monthly_household_income_band_million_krw)... -0.112   0.050  -2.257   \n",
       "12  CAT(monthly_household_income_band_million_krw)... -0.112   0.050  -2.213   \n",
       "13  CAT(monthly_household_income_band_million_krw)... -0.014   0.055  -0.261   \n",
       "14  CAT(monthly_household_income_band_million_krw)... -0.000   0.057  -0.006   \n",
       "15  CAT(monthly_household_income_band_million_krw)... -0.005   0.056  -0.086   \n",
       "16                 CAT(marital_status)[T.Not married]  0.015   0.023   0.662   \n",
       "17  CAT(marital_status)[T.Others (Divorced/Widowed...  0.019   0.037   0.507   \n",
       "18       CAT(employment_status)[T.Not in labor force]  0.015   0.038   0.408   \n",
       "19                    CAT(employment_status)[T.Other] -0.113   0.094  -1.205   \n",
       "20                CAT(employment_status)[T.Permanent]  0.004   0.030   0.143   \n",
       "21            CAT(employment_status)[T.Self-employed]  0.057   0.041   1.398   \n",
       "22       CAT(employment_status)[T.Unemployed/seeking]  0.000   0.048   0.003   \n",
       "23                  CAT(occupation_group)[T.Other/NA]  0.024   0.059   0.408   \n",
       "24        CAT(occupation_group)[T.Production/On-site] -0.048   0.035  -1.356   \n",
       "25   CAT(occupation_group)[T.Professional/Managerial] -0.053   0.032  -1.632   \n",
       "26             CAT(occupation_group)[T.Public sector]  0.031   0.035   0.870   \n",
       "27             CAT(occupation_group)[T.Service/Sales] -0.043   0.034  -1.266   \n",
       "28            CAT(union_membership)[T.Not applicable] -0.021   0.031  -0.680   \n",
       "29                       CAT(union_membership)[T.Yes]  0.008   0.025   0.325   \n",
       "30                                           exposure  0.341   0.022  15.856   \n",
       "31                                        beneficiary  0.359   0.022  16.630   \n",
       "32                                                age -0.000   0.001  -0.412   \n",
       "33                                      ideology_0_10 -0.009   0.005  -1.716   \n",
       "\n",
       "           p  sig  ci_low  ci_high  \n",
       "0          0  ***   3.824    4.114  \n",
       "1      0.732       -0.050    0.035  \n",
       "2      0.843       -0.141    0.115  \n",
       "3       0.24       -0.068    0.017  \n",
       "4      0.293       -0.035    0.115  \n",
       "5      0.799       -0.063    0.049  \n",
       "6      0.767       -0.044    0.060  \n",
       "7      0.508       -0.110    0.054  \n",
       "8     0.0459   **  -0.163   -0.002  \n",
       "9      0.353       -0.123    0.044  \n",
       "10      0.59       -0.112    0.063  \n",
       "11     0.024   **  -0.209   -0.015  \n",
       "12    0.0269   **  -0.211   -0.013  \n",
       "13     0.794       -0.123    0.094  \n",
       "14     0.996       -0.113    0.112  \n",
       "15     0.932       -0.114    0.104  \n",
       "16     0.508       -0.030    0.061  \n",
       "17     0.612       -0.054    0.092  \n",
       "18     0.684       -0.058    0.089  \n",
       "19     0.228       -0.297    0.071  \n",
       "20     0.887       -0.054    0.063  \n",
       "21     0.162       -0.023    0.137  \n",
       "22     0.997       -0.094    0.094  \n",
       "23     0.683       -0.091    0.139  \n",
       "24     0.175       -0.117    0.021  \n",
       "25     0.103       -0.116    0.011  \n",
       "26     0.384       -0.039    0.100  \n",
       "27     0.205       -0.109    0.023  \n",
       "28     0.496       -0.081    0.039  \n",
       "29     0.745       -0.040    0.056  \n",
       "30  1.28e-56  ***   0.299    0.383  \n",
       "31  4.23e-62  ***   0.316    0.401  \n",
       "32     0.681       -0.002    0.001  \n",
       "33    0.0862    *  -0.020    0.001  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **Formula:** `capital_tax_index ~ exposure + beneficiary + age + CAT(gender) + CAT(region) + CAT(education) + CAT(monthly_household_income_band_million_krw) + CAT(marital_status) + CAT(employment_status) + CAT(occupation_group) + CAT(union_membership) + ideology_0_10`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Model 2\n",
       "N = 3,000 | R² = 0.172 | Adj. R² = 0.163"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.036</td>\n",
       "      <td>0.075</td>\n",
       "      <td>54.140</td>\n",
       "      <td>0</td>\n",
       "      <td>***</td>\n",
       "      <td>3.890</td>\n",
       "      <td>4.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>0.771</td>\n",
       "      <td></td>\n",
       "      <td>-0.049</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/Prefer not to say]</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>0.848</td>\n",
       "      <td></td>\n",
       "      <td>-0.139</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-1.187</td>\n",
       "      <td>0.235</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.363</td>\n",
       "      <td></td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.435</td>\n",
       "      <td>0.664</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (enrolled)]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.892</td>\n",
       "      <td></td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.577</td>\n",
       "      <td>0.564</td>\n",
       "      <td></td>\n",
       "      <td>-0.106</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.995</td>\n",
       "      <td>0.046</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.926</td>\n",
       "      <td>0.354</td>\n",
       "      <td></td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>0.613</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-2.332</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.197</td>\n",
       "      <td>0.028</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>-0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.378</td>\n",
       "      <td>0.706</td>\n",
       "      <td></td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.891</td>\n",
       "      <td></td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.936</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Not married]</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.437</td>\n",
       "      <td></td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(marital_status)[T.Others (Divorced/Widowed...</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.541</td>\n",
       "      <td></td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_status)[T.Not in labor force]</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.583</td>\n",
       "      <td></td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_status)[T.Other]</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-1.077</td>\n",
       "      <td>0.281</td>\n",
       "      <td></td>\n",
       "      <td>-0.284</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_status)[T.Permanent]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.865</td>\n",
       "      <td></td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment_status)[T.Self-employed]</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.438</td>\n",
       "      <td>0.15</td>\n",
       "      <td></td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(employment_status)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>0.945</td>\n",
       "      <td></td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation_group)[T.Other/NA]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.814</td>\n",
       "      <td></td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation_group)[T.Production/On-site]</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.289</td>\n",
       "      <td>0.197</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation_group)[T.Professional/Managerial]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-1.590</td>\n",
       "      <td>0.112</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation_group)[T.Public sector]</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.397</td>\n",
       "      <td></td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAT(occupation_group)[T.Service/Sales]</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-1.212</td>\n",
       "      <td>0.225</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAT(union_membership)[T.Not applicable]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.950</td>\n",
       "      <td>0.342</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAT(union_membership)[T.Yes]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.649</td>\n",
       "      <td></td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>exposure</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.030</td>\n",
       "      <td>6.959</td>\n",
       "      <td>3.42e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>beneficiary</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.030</td>\n",
       "      <td>7.331</td>\n",
       "      <td>2.29e-13</td>\n",
       "      <td>***</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>exposure:beneficiary</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.043</td>\n",
       "      <td>6.452</td>\n",
       "      <td>1.1e-10</td>\n",
       "      <td>***</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>0.746</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-1.752</td>\n",
       "      <td>0.0798</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  4.036   0.075  54.140   \n",
       "1                                 CAT(gender)[T.Male] -0.006   0.022  -0.291   \n",
       "2              CAT(gender)[T.Other/Prefer not to say] -0.012   0.065  -0.192   \n",
       "3                   CAT(region)[T.Non-capital region] -0.026   0.022  -1.187   \n",
       "4                         CAT(education)[T.Graduate+]  0.035   0.038   0.910   \n",
       "5               CAT(education)[T.High school or less] -0.012   0.028  -0.435   \n",
       "6           CAT(education)[T.Some college (enrolled)]  0.004   0.027   0.136   \n",
       "7   CAT(monthly_household_income_band_million_krw)... -0.024   0.042  -0.577   \n",
       "8   CAT(monthly_household_income_band_million_krw)... -0.082   0.041  -1.995   \n",
       "9   CAT(monthly_household_income_band_million_krw)... -0.039   0.043  -0.926   \n",
       "10  CAT(monthly_household_income_band_million_krw)... -0.022   0.044  -0.506   \n",
       "11  CAT(monthly_household_income_band_million_krw)... -0.115   0.049  -2.332   \n",
       "12  CAT(monthly_household_income_band_million_krw)... -0.111   0.050  -2.197   \n",
       "13  CAT(monthly_household_income_band_million_krw)... -0.021   0.055  -0.378   \n",
       "14  CAT(monthly_household_income_band_million_krw)... -0.008   0.056  -0.136   \n",
       "15  CAT(monthly_household_income_band_million_krw)... -0.004   0.056  -0.081   \n",
       "16                 CAT(marital_status)[T.Not married]  0.018   0.023   0.778   \n",
       "17  CAT(marital_status)[T.Others (Divorced/Widowed...  0.023   0.037   0.611   \n",
       "18       CAT(employment_status)[T.Not in labor force]  0.021   0.037   0.550   \n",
       "19                    CAT(employment_status)[T.Other] -0.101   0.094  -1.077   \n",
       "20                CAT(employment_status)[T.Permanent]  0.005   0.030   0.169   \n",
       "21            CAT(employment_status)[T.Self-employed]  0.058   0.040   1.438   \n",
       "22       CAT(employment_status)[T.Unemployed/seeking] -0.003   0.048  -0.069   \n",
       "23                  CAT(occupation_group)[T.Other/NA]  0.014   0.058   0.235   \n",
       "24        CAT(occupation_group)[T.Production/On-site] -0.045   0.035  -1.289   \n",
       "25   CAT(occupation_group)[T.Professional/Managerial] -0.051   0.032  -1.590   \n",
       "26             CAT(occupation_group)[T.Public sector]  0.030   0.035   0.847   \n",
       "27             CAT(occupation_group)[T.Service/Sales] -0.041   0.034  -1.212   \n",
       "28            CAT(union_membership)[T.Not applicable] -0.029   0.031  -0.950   \n",
       "29                       CAT(union_membership)[T.Yes]  0.011   0.024   0.455   \n",
       "30                                           exposure  0.205   0.030   6.959   \n",
       "31                                        beneficiary  0.220   0.030   7.331   \n",
       "32                               exposure:beneficiary  0.275   0.043   6.452   \n",
       "33                                                age -0.000   0.001  -0.324   \n",
       "34                                      ideology_0_10 -0.010   0.005  -1.752   \n",
       "\n",
       "           p  sig  ci_low  ci_high  \n",
       "0          0  ***   3.890    4.182  \n",
       "1      0.771       -0.049    0.036  \n",
       "2      0.848       -0.139    0.114  \n",
       "3      0.235       -0.068    0.017  \n",
       "4      0.363       -0.040    0.110  \n",
       "5      0.664       -0.068    0.043  \n",
       "6      0.892       -0.048    0.056  \n",
       "7      0.564       -0.106    0.058  \n",
       "8      0.046   **  -0.162   -0.001  \n",
       "9      0.354       -0.123    0.044  \n",
       "10     0.613       -0.109    0.064  \n",
       "11    0.0197   **  -0.211   -0.018  \n",
       "12     0.028   **  -0.210   -0.012  \n",
       "13     0.706       -0.128    0.087  \n",
       "14     0.891       -0.118    0.103  \n",
       "15     0.936       -0.114    0.105  \n",
       "16     0.437       -0.027    0.063  \n",
       "17     0.541       -0.050    0.095  \n",
       "18     0.583       -0.053    0.094  \n",
       "19     0.281       -0.284    0.083  \n",
       "20     0.865       -0.053    0.063  \n",
       "21      0.15       -0.021    0.137  \n",
       "22     0.945       -0.097    0.090  \n",
       "23     0.814       -0.100    0.127  \n",
       "24     0.197       -0.114    0.023  \n",
       "25     0.112       -0.114    0.012  \n",
       "26     0.397       -0.039    0.098  \n",
       "27     0.225       -0.107    0.025  \n",
       "28     0.342       -0.089    0.031  \n",
       "29     0.649       -0.037    0.059  \n",
       "30  3.42e-12  ***   0.148    0.263  \n",
       "31  2.29e-13  ***   0.161    0.279  \n",
       "32   1.1e-10  ***   0.191    0.358  \n",
       "33     0.746       -0.002    0.001  \n",
       "34    0.0798    *  -0.020    0.001  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **Formula:** `capital_tax_index ~ exposure*beneficiary + age + CAT(gender) + CAT(region) + CAT(education) + CAT(monthly_household_income_band_million_krw) + CAT(marital_status) + CAT(employment_status) + CAT(occupation_group) + CAT(union_membership) + ideology_0_10`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Model 3\n",
       "N = 3,000 | R² = 0.192 | Adj. R² = 0.181"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.990</td>\n",
       "      <td>0.077</td>\n",
       "      <td>51.946</td>\n",
       "      <td>0</td>\n",
       "      <td>***</td>\n",
       "      <td>3.840</td>\n",
       "      <td>4.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.906</td>\n",
       "      <td></td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/Prefer not to say]</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.312</td>\n",
       "      <td>0.755</td>\n",
       "      <td></td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>0.276</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.349</td>\n",
       "      <td></td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.392</td>\n",
       "      <td>0.695</td>\n",
       "      <td></td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (enrolled)]</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.786</td>\n",
       "      <td></td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.487</td>\n",
       "      <td>0.626</td>\n",
       "      <td></td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-1.890</td>\n",
       "      <td>0.0587</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.903</td>\n",
       "      <td>0.366</td>\n",
       "      <td></td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.652</td>\n",
       "      <td>0.514</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-2.351</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.207</td>\n",
       "      <td>-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.056</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.763</td>\n",
       "      <td></td>\n",
       "      <td>-0.122</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>0.796</td>\n",
       "      <td></td>\n",
       "      <td>-0.124</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.921</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Not married]</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.437</td>\n",
       "      <td></td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(marital_status)[T.Others (Divorced/Widowed...</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.415</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_status)[T.Not in labor force]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.721</td>\n",
       "      <td></td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_status)[T.Other]</td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-1.072</td>\n",
       "      <td>0.284</td>\n",
       "      <td></td>\n",
       "      <td>-0.279</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_status)[T.Permanent]</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.972</td>\n",
       "      <td></td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment_status)[T.Self-employed]</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.361</td>\n",
       "      <td>0.173</td>\n",
       "      <td></td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(employment_status)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.814</td>\n",
       "      <td></td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation_group)[T.Other/NA]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.926</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation_group)[T.Production/On-site]</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.233</td>\n",
       "      <td>0.218</td>\n",
       "      <td></td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation_group)[T.Professional/Managerial]</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-1.761</td>\n",
       "      <td>0.0783</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation_group)[T.Public sector]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.471</td>\n",
       "      <td></td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAT(occupation_group)[T.Service/Sales]</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-1.323</td>\n",
       "      <td>0.186</td>\n",
       "      <td></td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAT(union_membership)[T.Not applicable]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAT(union_membership)[T.Yes]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.6</td>\n",
       "      <td></td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>exposure</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.043</td>\n",
       "      <td>5.679</td>\n",
       "      <td>1.36e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>beneficiary</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.044</td>\n",
       "      <td>6.389</td>\n",
       "      <td>1.67e-10</td>\n",
       "      <td>***</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>exposure:beneficiary</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.061</td>\n",
       "      <td>6.611</td>\n",
       "      <td>3.81e-11</td>\n",
       "      <td>***</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>homeowner</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1.539</td>\n",
       "      <td>0.124</td>\n",
       "      <td></td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>exposure:homeowner</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.059</td>\n",
       "      <td>-1.251</td>\n",
       "      <td>0.211</td>\n",
       "      <td></td>\n",
       "      <td>-0.189</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>beneficiary:homeowner</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-1.942</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>exposure:beneficiary:homeowner</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-2.702</td>\n",
       "      <td>0.00689</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.394</td>\n",
       "      <td>-0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>0.746</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-1.330</td>\n",
       "      <td>0.183</td>\n",
       "      <td></td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.990   0.077  51.946   \n",
       "1                                 CAT(gender)[T.Male] -0.003   0.021  -0.118   \n",
       "2              CAT(gender)[T.Other/Prefer not to say] -0.020   0.065  -0.312   \n",
       "3                   CAT(region)[T.Non-capital region] -0.023   0.021  -1.088   \n",
       "4                         CAT(education)[T.Graduate+]  0.035   0.038   0.937   \n",
       "5               CAT(education)[T.High school or less] -0.011   0.028  -0.392   \n",
       "6           CAT(education)[T.Some college (enrolled)]  0.007   0.026   0.271   \n",
       "7   CAT(monthly_household_income_band_million_krw)... -0.020   0.041  -0.487   \n",
       "8   CAT(monthly_household_income_band_million_krw)... -0.076   0.040  -1.890   \n",
       "9   CAT(monthly_household_income_band_million_krw)... -0.038   0.042  -0.903   \n",
       "10  CAT(monthly_household_income_band_million_krw)... -0.028   0.043  -0.652   \n",
       "11  CAT(monthly_household_income_band_million_krw)... -0.113   0.048  -2.351   \n",
       "12  CAT(monthly_household_income_band_million_krw)... -0.102   0.050  -2.056   \n",
       "13  CAT(monthly_household_income_band_million_krw)... -0.016   0.054  -0.302   \n",
       "14  CAT(monthly_household_income_band_million_krw)... -0.014   0.056  -0.258   \n",
       "15  CAT(monthly_household_income_band_million_krw)... -0.005   0.055  -0.099   \n",
       "16                 CAT(marital_status)[T.Not married]  0.018   0.023   0.777   \n",
       "17  CAT(marital_status)[T.Others (Divorced/Widowed...  0.030   0.037   0.816   \n",
       "18       CAT(employment_status)[T.Not in labor force]  0.013   0.037   0.357   \n",
       "19                    CAT(employment_status)[T.Other] -0.099   0.092  -1.072   \n",
       "20                CAT(employment_status)[T.Permanent]  0.001   0.029   0.035   \n",
       "21            CAT(employment_status)[T.Self-employed]  0.055   0.040   1.361   \n",
       "22       CAT(employment_status)[T.Unemployed/seeking] -0.011   0.047  -0.235   \n",
       "23                  CAT(occupation_group)[T.Other/NA]  0.005   0.057   0.093   \n",
       "24        CAT(occupation_group)[T.Production/On-site] -0.043   0.035  -1.233   \n",
       "25   CAT(occupation_group)[T.Professional/Managerial] -0.056   0.032  -1.761   \n",
       "26             CAT(occupation_group)[T.Public sector]  0.025   0.035   0.721   \n",
       "27             CAT(occupation_group)[T.Service/Sales] -0.044   0.033  -1.323   \n",
       "28            CAT(union_membership)[T.Not applicable] -0.029   0.030  -0.974   \n",
       "29                       CAT(union_membership)[T.Yes]  0.013   0.024   0.524   \n",
       "30                                           exposure  0.242   0.043   5.679   \n",
       "31                                        beneficiary  0.279   0.044   6.389   \n",
       "32                               exposure:beneficiary  0.400   0.061   6.611   \n",
       "33                                          homeowner  0.066   0.043   1.539   \n",
       "34                                 exposure:homeowner -0.074   0.059  -1.251   \n",
       "35                              beneficiary:homeowner -0.118   0.061  -1.942   \n",
       "36                     exposure:beneficiary:homeowner -0.228   0.085  -2.702   \n",
       "37                                                age -0.000   0.001  -0.325   \n",
       "38                                      ideology_0_10 -0.007   0.005  -1.330   \n",
       "\n",
       "           p  sig  ci_low  ci_high  \n",
       "0          0  ***   3.840    4.141  \n",
       "1      0.906       -0.044    0.039  \n",
       "2      0.755       -0.148    0.107  \n",
       "3      0.276       -0.065    0.019  \n",
       "4      0.349       -0.039    0.109  \n",
       "5      0.695       -0.066    0.044  \n",
       "6      0.786       -0.045    0.059  \n",
       "7      0.626       -0.101    0.060  \n",
       "8     0.0587    *  -0.155    0.003  \n",
       "9      0.366       -0.120    0.044  \n",
       "10     0.514       -0.113    0.057  \n",
       "11    0.0187   **  -0.207   -0.019  \n",
       "12    0.0397   **  -0.200   -0.005  \n",
       "13     0.763       -0.122    0.089  \n",
       "14     0.796       -0.124    0.095  \n",
       "15     0.921       -0.113    0.102  \n",
       "16     0.437       -0.027    0.063  \n",
       "17     0.415       -0.042    0.101  \n",
       "18     0.721       -0.060    0.086  \n",
       "19     0.284       -0.279    0.082  \n",
       "20     0.972       -0.057    0.059  \n",
       "21     0.173       -0.024    0.133  \n",
       "22     0.814       -0.104    0.081  \n",
       "23     0.926       -0.107    0.118  \n",
       "24     0.218       -0.111    0.025  \n",
       "25    0.0783    *  -0.118    0.006  \n",
       "26     0.471       -0.043    0.093  \n",
       "27     0.186       -0.108    0.021  \n",
       "28      0.33       -0.088    0.030  \n",
       "29       0.6       -0.035    0.060  \n",
       "30  1.36e-08  ***   0.158    0.326  \n",
       "31  1.67e-10  ***   0.193    0.365  \n",
       "32  3.81e-11  ***   0.282    0.519  \n",
       "33     0.124       -0.018    0.150  \n",
       "34     0.211       -0.189    0.042  \n",
       "35    0.0522    *  -0.237    0.001  \n",
       "36   0.00689  ***  -0.394   -0.063  \n",
       "37     0.746       -0.002    0.001  \n",
       "38     0.183       -0.018    0.003  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **Formula:** `capital_tax_index ~ exposure*beneficiary*homeowner + age + CAT(gender) + CAT(region) + CAT(education) + CAT(monthly_household_income_band_million_krw) + CAT(marital_status) + CAT(employment_status) + CAT(occupation_group) + CAT(union_membership) + ideology_0_10`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Model 4\n",
       "N = 3,000 | R² = 0.187 | Adj. R² = 0.176"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.029</td>\n",
       "      <td>0.079</td>\n",
       "      <td>50.794</td>\n",
       "      <td>0</td>\n",
       "      <td>***</td>\n",
       "      <td>3.874</td>\n",
       "      <td>4.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(wealth_tertile)[T.Middle]</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.415</td>\n",
       "      <td>0.678</td>\n",
       "      <td></td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(wealth_tertile)[T.High]</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.92</td>\n",
       "      <td></td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.411</td>\n",
       "      <td>0.681</td>\n",
       "      <td></td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(gender)[T.Other/Prefer not to say]</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.287</td>\n",
       "      <td>0.774</td>\n",
       "      <td></td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-1.236</td>\n",
       "      <td>0.216</td>\n",
       "      <td></td>\n",
       "      <td>-0.069</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.396</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.453</td>\n",
       "      <td>0.65</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(education)[T.Some college (enrolled)]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.886</td>\n",
       "      <td></td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.594</td>\n",
       "      <td>0.552</td>\n",
       "      <td></td>\n",
       "      <td>-0.106</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.880</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.157</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.898</td>\n",
       "      <td>0.369</td>\n",
       "      <td></td>\n",
       "      <td>-0.121</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.705</td>\n",
       "      <td>0.481</td>\n",
       "      <td></td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-2.185</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.216</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>-0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.341</td>\n",
       "      <td>0.733</td>\n",
       "      <td></td>\n",
       "      <td>-0.126</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>0.771</td>\n",
       "      <td></td>\n",
       "      <td>-0.127</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(monthly_household_income_band_million_krw)...</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.993</td>\n",
       "      <td></td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(marital_status)[T.Not married]</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.268</td>\n",
       "      <td></td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(marital_status)[T.Others (Divorced/Widowed...</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.368</td>\n",
       "      <td></td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_status)[T.Not in labor force]</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.647</td>\n",
       "      <td></td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment_status)[T.Other]</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-1.046</td>\n",
       "      <td>0.295</td>\n",
       "      <td></td>\n",
       "      <td>-0.277</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(employment_status)[T.Permanent]</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.822</td>\n",
       "      <td></td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(employment_status)[T.Self-employed]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.136</td>\n",
       "      <td></td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(employment_status)[T.Unemployed/seeking]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.972</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation_group)[T.Other/NA]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.792</td>\n",
       "      <td></td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation_group)[T.Production/On-site]</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.201</td>\n",
       "      <td>0.23</td>\n",
       "      <td></td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAT(occupation_group)[T.Professional/Managerial]</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-1.452</td>\n",
       "      <td>0.147</td>\n",
       "      <td></td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAT(occupation_group)[T.Public sector]</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.38</td>\n",
       "      <td></td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAT(occupation_group)[T.Service/Sales]</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-1.102</td>\n",
       "      <td>0.27</td>\n",
       "      <td></td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CAT(union_membership)[T.Not applicable]</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.769</td>\n",
       "      <td>0.442</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CAT(union_membership)[T.Yes]</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.482</td>\n",
       "      <td></td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>exposure</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.052</td>\n",
       "      <td>3.913</td>\n",
       "      <td>9.11e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>exposure:CAT(wealth_tertile)[T.Middle]</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.971</td>\n",
       "      <td></td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>exposure:CAT(wealth_tertile)[T.High]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.893</td>\n",
       "      <td></td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>beneficiary</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.053</td>\n",
       "      <td>3.992</td>\n",
       "      <td>6.56e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>beneficiary:CAT(wealth_tertile)[T.Middle]</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.45</td>\n",
       "      <td></td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>beneficiary:CAT(wealth_tertile)[T.High]</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.737</td>\n",
       "      <td></td>\n",
       "      <td>-0.171</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>exposure:beneficiary</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.072</td>\n",
       "      <td>6.825</td>\n",
       "      <td>8.81e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>exposure:beneficiary:CAT(wealth_tertile)[T.Mid...</td>\n",
       "      <td>-0.283</td>\n",
       "      <td>0.104</td>\n",
       "      <td>-2.734</td>\n",
       "      <td>0.00625</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.486</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>exposure:beneficiary:CAT(wealth_tertile)[T.High]</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-3.432</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.558</td>\n",
       "      <td>-0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.817</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-1.593</td>\n",
       "      <td>0.111</td>\n",
       "      <td></td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  4.029   0.079  50.794   \n",
       "1                       CAT(wealth_tertile)[T.Middle] -0.023   0.055  -0.415   \n",
       "2                         CAT(wealth_tertile)[T.High] -0.005   0.052  -0.100   \n",
       "3                                 CAT(gender)[T.Male] -0.009   0.021  -0.411   \n",
       "4              CAT(gender)[T.Other/Prefer not to say] -0.019   0.065  -0.287   \n",
       "5                   CAT(region)[T.Non-capital region] -0.027   0.021  -1.236   \n",
       "6                         CAT(education)[T.Graduate+]  0.032   0.038   0.850   \n",
       "7               CAT(education)[T.High school or less] -0.013   0.028  -0.453   \n",
       "8           CAT(education)[T.Some college (enrolled)]  0.004   0.026   0.143   \n",
       "9   CAT(monthly_household_income_band_million_krw)... -0.025   0.042  -0.594   \n",
       "10  CAT(monthly_household_income_band_million_krw)... -0.077   0.041  -1.880   \n",
       "11  CAT(monthly_household_income_band_million_krw)... -0.038   0.042  -0.898   \n",
       "12  CAT(monthly_household_income_band_million_krw)... -0.031   0.044  -0.705   \n",
       "13  CAT(monthly_household_income_band_million_krw)... -0.107   0.049  -2.185   \n",
       "14  CAT(monthly_household_income_band_million_krw)... -0.111   0.050  -2.216   \n",
       "15  CAT(monthly_household_income_band_million_krw)... -0.019   0.055  -0.341   \n",
       "16  CAT(monthly_household_income_band_million_krw)... -0.016   0.056  -0.291   \n",
       "17  CAT(monthly_household_income_band_million_krw)... -0.000   0.056  -0.008   \n",
       "18                 CAT(marital_status)[T.Not married]  0.026   0.023   1.108   \n",
       "19  CAT(marital_status)[T.Others (Divorced/Widowed...  0.033   0.037   0.900   \n",
       "20       CAT(employment_status)[T.Not in labor force]  0.017   0.037   0.458   \n",
       "21                    CAT(employment_status)[T.Other] -0.096   0.092  -1.046   \n",
       "22                CAT(employment_status)[T.Permanent]  0.007   0.029   0.225   \n",
       "23            CAT(employment_status)[T.Self-employed]  0.060   0.040   1.492   \n",
       "24       CAT(employment_status)[T.Unemployed/seeking]  0.002   0.047   0.036   \n",
       "25                  CAT(occupation_group)[T.Other/NA]  0.015   0.058   0.264   \n",
       "26        CAT(occupation_group)[T.Production/On-site] -0.042   0.035  -1.201   \n",
       "27   CAT(occupation_group)[T.Professional/Managerial] -0.047   0.032  -1.452   \n",
       "28             CAT(occupation_group)[T.Public sector]  0.031   0.035   0.878   \n",
       "29             CAT(occupation_group)[T.Service/Sales] -0.037   0.033  -1.102   \n",
       "30            CAT(union_membership)[T.Not applicable] -0.023   0.030  -0.769   \n",
       "31                       CAT(union_membership)[T.Yes]  0.017   0.024   0.703   \n",
       "32                                           exposure  0.203   0.052   3.913   \n",
       "33             exposure:CAT(wealth_tertile)[T.Middle] -0.003   0.073  -0.036   \n",
       "34               exposure:CAT(wealth_tertile)[T.High]  0.010   0.073   0.135   \n",
       "35                                        beneficiary  0.210   0.053   3.992   \n",
       "36          beneficiary:CAT(wealth_tertile)[T.Middle]  0.056   0.075   0.755   \n",
       "37            beneficiary:CAT(wealth_tertile)[T.High] -0.025   0.074  -0.336   \n",
       "38                               exposure:beneficiary  0.490   0.072   6.825   \n",
       "39  exposure:beneficiary:CAT(wealth_tertile)[T.Mid... -0.283   0.104  -2.734   \n",
       "40   exposure:beneficiary:CAT(wealth_tertile)[T.High] -0.355   0.103  -3.432   \n",
       "41                                                age -0.000   0.001  -0.232   \n",
       "42                                      ideology_0_10 -0.009   0.005  -1.593   \n",
       "\n",
       "           p  sig  ci_low  ci_high  \n",
       "0          0  ***   3.874    4.184  \n",
       "1      0.678       -0.130    0.084  \n",
       "2       0.92       -0.108    0.097  \n",
       "3      0.681       -0.051    0.033  \n",
       "4      0.774       -0.147    0.109  \n",
       "5      0.216       -0.069    0.016  \n",
       "6      0.396       -0.042    0.107  \n",
       "7       0.65       -0.068    0.042  \n",
       "8      0.886       -0.048    0.056  \n",
       "9      0.552       -0.106    0.057  \n",
       "10    0.0601    *  -0.157    0.003  \n",
       "11     0.369       -0.121    0.045  \n",
       "12     0.481       -0.118    0.055  \n",
       "13    0.0289   **  -0.204   -0.011  \n",
       "14    0.0267   **  -0.209   -0.013  \n",
       "15     0.733       -0.126    0.088  \n",
       "16     0.771       -0.127    0.094  \n",
       "17     0.993       -0.110    0.109  \n",
       "18     0.268       -0.020    0.071  \n",
       "19     0.368       -0.039    0.105  \n",
       "20     0.647       -0.056    0.090  \n",
       "21     0.295       -0.277    0.084  \n",
       "22     0.822       -0.051    0.064  \n",
       "23     0.136       -0.019    0.139  \n",
       "24     0.972       -0.091    0.094  \n",
       "25     0.792       -0.098    0.128  \n",
       "26      0.23       -0.110    0.026  \n",
       "27     0.147       -0.110    0.016  \n",
       "28      0.38       -0.038    0.099  \n",
       "29      0.27       -0.102    0.028  \n",
       "30     0.442       -0.083    0.036  \n",
       "31     0.482       -0.031    0.065  \n",
       "32  9.11e-05  ***   0.101    0.304  \n",
       "33     0.971       -0.146    0.140  \n",
       "34     0.893       -0.133    0.152  \n",
       "35  6.56e-05  ***   0.107    0.313  \n",
       "36      0.45       -0.090    0.203  \n",
       "37     0.737       -0.171    0.121  \n",
       "38  8.81e-12  ***   0.349    0.630  \n",
       "39   0.00625  ***  -0.486   -0.080  \n",
       "40    0.0006  ***  -0.558   -0.152  \n",
       "41     0.817       -0.002    0.001  \n",
       "42     0.111       -0.019    0.002  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- **Formula:** `capital_tax_index ~ exposure*beneficiary*CAT(wealth_tertile) + age + CAT(gender) + CAT(region) + CAT(education) + CAT(monthly_household_income_band_million_krw) + CAT(marital_status) + CAT(employment_status) + CAT(occupation_group) + CAT(union_membership) + ideology_0_10`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "✅ Done: 4 full regression tables displayed."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\72_AI_세금\\results_models_1to4.xlsx\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# 1) Variables (Paper 2)\n",
    "# ============================================================\n",
    "A  = \"exposure\"          # 1=High risk, 0=Low\n",
    "B  = \"beneficiary\"       # 1=Concentrated capital gains, 0=Broad gains\n",
    "DV = \"capital_tax_index\" # primary DV (mean of 3 items)\n",
    "\n",
    "# Moderators\n",
    "HOME_DUMMY = \"homeowner\"       # 1=Owner, 0=Renter/Other\n",
    "WEALTH_T   = \"wealth_tertile\"  # Low/Middle/High (categorical)\n",
    "\n",
    "# ============================================================\n",
    "# 2) Pre-processing (safe coercions)\n",
    "# ============================================================\n",
    "# Ensure A/B are numeric 0/1\n",
    "for c in [A, B]:\n",
    "    df[c] = pd.to_numeric(df[c], errors=\"coerce\").astype(\"Int64\")\n",
    "\n",
    "# Homeowner dummy (if only homeownership string exists, derive homeowner)\n",
    "if HOME_DUMMY not in df.columns:\n",
    "    if \"homeownership\" in df.columns:\n",
    "        df[HOME_DUMMY] = (df[\"homeownership\"].astype(str).str.strip().str.lower() == \"owner\").astype(int)\n",
    "    else:\n",
    "        raise ValueError(\"homeowner 또는 homeownership 변수가 없습니다. 변수명을 확인하세요.\")\n",
    "\n",
    "df[HOME_DUMMY] = pd.to_numeric(df[HOME_DUMMY], errors=\"coerce\").astype(\"Int64\")\n",
    "\n",
    "# Wealth tertile must be categorical with 3 levels\n",
    "if WEALTH_T not in df.columns:\n",
    "    raise ValueError(\"wealth_tertile 변수가 없습니다. (Low/Middle/High) 변수명을 확인하세요.\")\n",
    "df[WEALTH_T] = df[WEALTH_T].astype(str).str.strip()\n",
    "# Optional: enforce ordered category\n",
    "df[WEALTH_T] = pd.Categorical(df[WEALTH_T], categories=[\"Low\", \"Middle\", \"High\"], ordered=True)\n",
    "\n",
    "# Ideology should be integer 0–10 (asked last)\n",
    "if \"ideology_0_10\" in df.columns:\n",
    "    df[\"ideology_0_10\"] = pd.to_numeric(df[\"ideology_0_10\"], errors=\"coerce\")\n",
    "    df[\"ideology_0_10\"] = df[\"ideology_0_10\"].round().clip(0, 10).astype(\"Int64\")\n",
    "\n",
    "# ============================================================\n",
    "# 3) Controls (use CAT() to avoid patsy C() collision)\n",
    "#    Update variable names here if your df uses different names\n",
    "# ============================================================\n",
    "CAT = pb.C\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_household_income_band_million_krw)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(employment_status)\",\n",
    "    \"CAT(occupation_group)\",\n",
    "    \"CAT(union_membership)\",\n",
    "    \"ideology_0_10\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "# ============================================================\n",
    "# 4) Helpers (fit + full table)\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    # HC3 robust SE (good default for survey-type heteroskedasticity)\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def full_table(m):\n",
    "    out = pd.DataFrame({\n",
    "        \"term\": m.params.index,\n",
    "        \"coef\": m.params.values,\n",
    "        \"se_HC3\": m.bse.values,\n",
    "        \"t\": m.tvalues.values,\n",
    "        \"p\": m.pvalues.values,\n",
    "    })\n",
    "    out[\"ci_low\"]  = out[\"coef\"] - 1.96*out[\"se_HC3\"]\n",
    "    out[\"ci_high\"] = out[\"coef\"] + 1.96*out[\"se_HC3\"]\n",
    "    return out\n",
    "\n",
    "def add_sig_stars(p):\n",
    "    if p < 0.01: return \"***\"\n",
    "    if p < 0.05: return \"**\"\n",
    "    if p < 0.1:  return \"*\"\n",
    "    return \"\"\n",
    "\n",
    "def pretty(df_in, digits=3):\n",
    "    df2 = df_in.copy()\n",
    "    df2[\"sig\"] = df2[\"p\"].apply(add_sig_stars)\n",
    "    for c in [\"coef\",\"se_HC3\",\"t\",\"ci_low\",\"ci_high\"]:\n",
    "        df2[c] = df2[c].map(lambda x: round(float(x), digits))\n",
    "    df2[\"p\"] = df2[\"p\"].map(lambda x: f\"{x:.3g}\")\n",
    "    return df2[[\"term\",\"coef\",\"se_HC3\",\"t\",\"p\",\"sig\",\"ci_low\",\"ci_high\"]]\n",
    "\n",
    "def model_stats_line(m):\n",
    "    return f\"N = {int(m.nobs):,} | R² = {m.rsquared:.3f} | Adj. R² = {m.rsquared_adj:.3f}\"\n",
    "\n",
    "def show_model(title, m):\n",
    "    display(Markdown(f\"### {title}\\n{model_stats_line(m)}\"))\n",
    "    display(pretty(full_table(m)))\n",
    "\n",
    "# ============================================================\n",
    "# 5) Model specs (1–4)\n",
    "# ============================================================\n",
    "# Model 1: No interaction\n",
    "f1 = f\"{DV} ~ {A} + {B} + {CONTROLS}\"\n",
    "\n",
    "# Model 2: Add Risk×Concentration\n",
    "f2 = f\"{DV} ~ {A}*{B} + {CONTROLS}\"\n",
    "\n",
    "# Model 3: Add Homeownership 3-way (attenuation hypothesis)\n",
    "# IMPORTANT: include lower-order terms automatically via * *\n",
    "f3 = f\"{DV} ~ {A}*{B}*{HOME_DUMMY} + {CONTROLS}\"\n",
    "\n",
    "# Model 4: Add Wealth 3-way (wealth tertile categorical)\n",
    "# This yields two 3-way terms: for Middle and High (vs Low reference)\n",
    "f4 = f\"{DV} ~ {A}*{B}*CAT({WEALTH_T}) + {CONTROLS}\"\n",
    "\n",
    "models = []\n",
    "for i, f in enumerate([f1, f2, f3, f4], start=1):\n",
    "    m = fit_ols(f, df)\n",
    "    models.append((i, f, m))\n",
    "\n",
    "# ============================================================\n",
    "# 6) Display full results (Models 1–4)\n",
    "# ============================================================\n",
    "display(Markdown(\"## Full regression results (Models 1–4)\"))\n",
    "\n",
    "for i, f, m in models:\n",
    "    show_model(f\"Model {i}\", m)\n",
    "    display(Markdown(f\"- **Formula:** `{f}`\"))\n",
    "\n",
    "display(Markdown(\"✅ Done: 4 full regression tables displayed.\"))\n",
    "\n",
    "# ============================================================\n",
    "# 7) Export all results to Excel\n",
    "# ============================================================\n",
    "out_xlsx = os.path.join(DATA_DIR, \"results_models_1to4.xlsx\")\n",
    "with pd.ExcelWriter(out_xlsx, engine=\"openpyxl\") as writer:\n",
    "    for i, f, m in models:\n",
    "        tbl = pretty(full_table(m))\n",
    "        # add model info rows on top (optional)\n",
    "        info = pd.DataFrame({\n",
    "            \"term\": [\"MODEL_INFO\", \"FORMULA\"],\n",
    "            \"coef\": [model_stats_line(m), f],\n",
    "            \"se_HC3\": [\"\", \"\"],\n",
    "            \"t\": [\"\", \"\"],\n",
    "            \"p\": [\"\", \"\"],\n",
    "            \"sig\": [\"\", \"\"],\n",
    "            \"ci_low\": [\"\", \"\"],\n",
    "            \"ci_high\": [\"\", \"\"],\n",
    "        })\n",
    "        export = pd.concat([info, tbl], ignore_index=True)\n",
    "        export.to_excel(writer, sheet_name=f\"Model_{i}\", index=False)\n",
    "\n",
    "print(\"Saved:\", out_xlsx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6970d449-19df-4d46-88b1-87d763065205",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "18111d9f-21da-4b1d-b608-ef66e57ce2ca",
   "metadata": {},
   "source": [
    "## 2. Figure 1. Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62d8dc6a-7fc3-49c3-9b19-64c9c64eee35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CAT = pb.C\n",
    "\n",
    "# Save high-resolution PNG\n",
    "# -----------------------------\n",
    "output_path = r\"E:\\\\Figure_1_predicted.png\"\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Load + Fit (필요 시)\n",
    "# -----------------------------\n",
    "# df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "A  = \"exposure\"\n",
    "B  = \"beneficiary\"\n",
    "DV = \"capital_tax_index\"\n",
    "HOME = \"homeowner\"\n",
    "WEALTH = \"wealth_tertile\"\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_household_income_band_million_krw)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(employment_status)\",\n",
    "    \"CAT(occupation_group)\",\n",
    "    \"CAT(union_membership)\",\n",
    "    \"ideology_0_10\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "# Model 3: A*B*homeowner + controls\n",
    "f3 = f\"{DV} ~ {A}*{B}*{HOME} + {CONTROLS}\"\n",
    "m3 = fit_ols(f3, df)\n",
    "\n",
    "# Model 4: A*B*C(wealth_tertile) + controls\n",
    "f4 = f\"{DV} ~ {A}*{B}*CAT({WEALTH}) + {CONTROLS}\"\n",
    "m4 = fit_ols(f4, df)\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Baseline row \n",
    "# -----------------------------\n",
    "def mode_or_first(s):\n",
    "    s = s.dropna()\n",
    "    if len(s) == 0:\n",
    "        return np.nan\n",
    "    return s.mode().iloc[0] if not s.mode().empty else s.iloc[0]\n",
    "\n",
    "baseline = {}\n",
    "baseline[\"age\"] = int(round(pd.to_numeric(df[\"age\"], errors=\"coerce\").mean()))\n",
    "baseline[\"ideology_0_10\"] = int(round(pd.to_numeric(df[\"ideology_0_10\"], errors=\"coerce\").median()))\n",
    "\n",
    "for c in [\"gender\",\"region\",\"education\",\"monthly_household_income_band_million_krw\",\n",
    "          \"marital_status\",\"employment_status\",\"occupation_group\",\"union_membership\"]:\n",
    "    baseline[c] = mode_or_first(df[c].astype(str))\n",
    "\n",
    "baseline[WEALTH] = \"Middle\" if \"Middle\" in df[WEALTH].astype(str).unique() else mode_or_first(df[WEALTH].astype(str))\n",
    "\n",
    "# -----------------------------\n",
    "# -----------------------------\n",
    "risk_levels = [0, 1]\n",
    "x = np.arange(len(risk_levels))\n",
    "x_labels = [\"Low risk\", \"High risk\"]\n",
    "\n",
    "def build_scenarios_home_risk():\n",
    "    rows = []\n",
    "    for h in [0, 1]:\n",
    "        for b in [0, 1]:\n",
    "            for a in risk_levels:\n",
    "                r = baseline.copy()\n",
    "                r[A] = a\n",
    "                r[B] = b\n",
    "                r[HOME] = h\n",
    "                rows.append(r)\n",
    "    scen = pd.DataFrame(rows)\n",
    "    scen[WEALTH] = pd.Categorical(scen[WEALTH], categories=[\"Low\",\"Middle\",\"High\"], ordered=True)\n",
    "    return scen\n",
    "\n",
    "def build_scenarios_wealth_risk(w):\n",
    "    rows = []\n",
    "    for b in [0, 1]:\n",
    "        for a in risk_levels:\n",
    "            r = baseline.copy()\n",
    "            r[A] = a\n",
    "            r[B] = b\n",
    "            r[WEALTH] = w\n",
    "            rows.append(r)\n",
    "    scen = pd.DataFrame(rows)\n",
    "    scen[WEALTH] = pd.Categorical(scen[WEALTH], categories=[\"Low\",\"Middle\",\"High\"], ordered=True)\n",
    "    return scen\n",
    "\n",
    "# -----------------------------\n",
    "# 3) 예측 + CI 추출\n",
    "# -----------------------------\n",
    "def pred_ci(model, newdf):\n",
    "    pr = model.get_prediction(newdf).summary_frame(alpha=0.05)\n",
    "    out = newdf.copy()\n",
    "    out[\"pred\"] = pr[\"mean\"].values\n",
    "    out[\"ci_low\"] = pr[\"mean_ci_lower\"].values\n",
    "    out[\"ci_high\"] = pr[\"mean_ci_upper\"].values\n",
    "    return out\n",
    "\n",
    "sc_home = build_scenarios_home_risk()\n",
    "ph = pred_ci(m3, sc_home)\n",
    "\n",
    "pw_low  = pred_ci(m4, build_scenarios_wealth_risk(\"Low\"))\n",
    "pw_high = pred_ci(m4, build_scenarios_wealth_risk(\"High\"))\n",
    "\n",
    "def subset_pred(df_pred, homeowner=None, wealth=None, beneficiary=None):\n",
    "    out = df_pred.copy()\n",
    "    if homeowner is not None:\n",
    "        out = out[out[HOME] == homeowner]\n",
    "    if wealth is not None:\n",
    "        out = out[out[WEALTH].astype(str) == wealth]\n",
    "    if beneficiary is not None:\n",
    "        out = out[out[B] == beneficiary]\n",
    "    return out.sort_values(by=[A]).reset_index(drop=True)\n",
    "\n",
    "# -----------------------------\n",
    "# 4) Plot: 1×2 subplots (흑백 대응: marker 모양으로 구분)\n",
    "# -----------------------------\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
    "\n",
    "label_b0 = \"Broad gains\"\n",
    "label_b1 = \"Concentrated gains\"\n",
    "\n",
    "# ✅ 흑백 구분용 marker 지정\n",
    "marker_b0 = \"o\"   # Broad = circle\n",
    "marker_b1 = \"^\"   # Concentrated = triangle\n",
    "\n",
    "# ---- (a) Homeownership panel\n",
    "for h, ls, hlabel in [(0, \"--\", \"Renter/Other\"), (1, \"-\", \"Owner\")]:\n",
    "    d_b0 = subset_pred(ph, homeowner=h, beneficiary=0)\n",
    "    d_b1 = subset_pred(ph, homeowner=h, beneficiary=1)\n",
    "\n",
    "    axes[0].plot(\n",
    "        x, d_b0[\"pred\"],\n",
    "        marker=marker_b0, linestyle=ls,\n",
    "        label=f\"{hlabel} — {label_b0}\"\n",
    "    )\n",
    "    axes[0].fill_between(x, d_b0[\"ci_low\"], d_b0[\"ci_high\"], alpha=0.15)\n",
    "\n",
    "    axes[0].plot(\n",
    "        x, d_b1[\"pred\"],\n",
    "        marker=marker_b1, linestyle=ls,\n",
    "        label=f\"{hlabel} — {label_b1}\"\n",
    "    )\n",
    "    axes[0].fill_between(x, d_b1[\"ci_low\"], d_b1[\"ci_high\"], alpha=0.15)\n",
    "\n",
    "axes[0].set_xticks(x)\n",
    "axes[0].set_xticklabels(x_labels, fontsize=11)\n",
    "axes[0].tick_params(axis=\"y\", labelsize=11)\n",
    "axes[0].set_title(\"(a) Predicted capital tax index\\nby Homeownership\", pad=14, size=16)\n",
    "axes[0].set_ylabel(\"Predicted capital tax index (1–7)\", fontsize=13)\n",
    "axes[0].grid(True, linestyle=\"--\", linewidth=0.6, alpha=0.6)\n",
    "axes[0].legend(frameon=False, fontsize=9)\n",
    "\n",
    "# ---- (b) Wealth panel (Low vs High)\n",
    "for w, ls in [(\"Low\", \"--\"), (\"High\", \"-\")]:\n",
    "    d = pw_low if w == \"Low\" else pw_high\n",
    "    d_b0 = subset_pred(d, wealth=w, beneficiary=0)\n",
    "    d_b1 = subset_pred(d, wealth=w, beneficiary=1)\n",
    "\n",
    "    axes[1].plot(\n",
    "        x, d_b0[\"pred\"],\n",
    "        marker=marker_b0, linestyle=ls,\n",
    "        label=f\"{w} wealth — {label_b0}\"\n",
    "    )\n",
    "    axes[1].fill_between(x, d_b0[\"ci_low\"], d_b0[\"ci_high\"], alpha=0.15)\n",
    "\n",
    "    axes[1].plot(\n",
    "        x, d_b1[\"pred\"],\n",
    "        marker=marker_b1, linestyle=ls,\n",
    "        label=f\"{w} wealth — {label_b1}\"\n",
    "    )\n",
    "    axes[1].fill_between(x, d_b1[\"ci_low\"], d_b1[\"ci_high\"], alpha=0.15)\n",
    "\n",
    "axes[1].set_xticks(x)\n",
    "axes[1].set_xticklabels(x_labels, fontsize=11)\n",
    "axes[1].tick_params(axis=\"y\", labelsize=11)\n",
    "axes[1].set_title(\"(b) Predicted capital tax index\\nby Wealth (Low vs High)\", pad=14, size=16)\n",
    "axes[1].grid(True, linestyle=\"--\", linewidth=0.6, alpha=0.6)\n",
    "axes[1].legend(frameon=False, fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(output_path, dpi=600, bbox_inches=\"tight\", format=\"png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6828cf-7798-446a-b84b-27f6663fbb88",
   "metadata": {},
   "source": [
    "## 2.1 Predicted full results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "460d85a7-75f9-4c41-ad06-e3ac10ed528a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Homeownership</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Renter/Other</td>\n",
       "      <td>Low</td>\n",
       "      <td>Broad</td>\n",
       "      <td>3.799803</td>\n",
       "      <td>3.699352</td>\n",
       "      <td>3.900253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Renter/Other</td>\n",
       "      <td>High</td>\n",
       "      <td>Broad</td>\n",
       "      <td>4.041791</td>\n",
       "      <td>3.949198</td>\n",
       "      <td>4.134384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renter/Other</td>\n",
       "      <td>Low</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.078777</td>\n",
       "      <td>3.980021</td>\n",
       "      <td>4.177532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Renter/Other</td>\n",
       "      <td>High</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.720978</td>\n",
       "      <td>4.623405</td>\n",
       "      <td>4.818552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Owner</td>\n",
       "      <td>Low</td>\n",
       "      <td>Broad</td>\n",
       "      <td>3.865984</td>\n",
       "      <td>3.768677</td>\n",
       "      <td>3.963291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Owner</td>\n",
       "      <td>High</td>\n",
       "      <td>Broad</td>\n",
       "      <td>4.034284</td>\n",
       "      <td>3.941287</td>\n",
       "      <td>4.127282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Owner</td>\n",
       "      <td>Low</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.027213</td>\n",
       "      <td>3.930664</td>\n",
       "      <td>4.123763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Owner</td>\n",
       "      <td>High</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.367378</td>\n",
       "      <td>4.269060</td>\n",
       "      <td>4.465695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Homeownership  Risk Concentration  Predicted    CI_low   CI_high\n",
       "0  Renter/Other   Low         Broad   3.799803  3.699352  3.900253\n",
       "1  Renter/Other  High         Broad   4.041791  3.949198  4.134384\n",
       "2  Renter/Other   Low  Concentrated   4.078777  3.980021  4.177532\n",
       "3  Renter/Other  High  Concentrated   4.720978  4.623405  4.818552\n",
       "4         Owner   Low         Broad   3.865984  3.768677  3.963291\n",
       "5         Owner  High         Broad   4.034284  3.941287  4.127282\n",
       "6         Owner   Low  Concentrated   4.027213  3.930664  4.123763\n",
       "7         Owner  High  Concentrated   4.367378  4.269060  4.465695"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pred_table_home(model):\n",
    "    rows = []\n",
    "\n",
    "    for h in [0, 1]:\n",
    "        for b in [0, 1]:\n",
    "            for a in [0, 1]:\n",
    "\n",
    "                r = baseline.copy()\n",
    "                r[A] = a\n",
    "                r[B] = b\n",
    "                r[HOME] = h\n",
    "\n",
    "                newdf = pd.DataFrame([r])\n",
    "                pr = model.get_prediction(newdf).summary_frame(alpha=0.05)\n",
    "\n",
    "                rows.append({\n",
    "                    \"Homeownership\": \"Owner\" if h == 1 else \"Renter/Other\",\n",
    "                    \"Risk\": \"High\" if a == 1 else \"Low\",\n",
    "                    \"Concentration\": \"Concentrated\" if b == 1 else \"Broad\",\n",
    "                    \"Predicted\": pr[\"mean\"].iloc[0],\n",
    "                    \"CI_low\": pr[\"mean_ci_lower\"].iloc[0],\n",
    "                    \"CI_high\": pr[\"mean_ci_upper\"].iloc[0]\n",
    "                })\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "home_pred_table = pred_table_home(m3)\n",
    "home_pred_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5109e6e1-19d6-4a53-9fc8-27c189bf42a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48035b0e-d64d-4d41-9f6b-f4486ca7c746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Broad</td>\n",
       "      <td>3.843515</td>\n",
       "      <td>3.733141</td>\n",
       "      <td>3.953888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Broad</td>\n",
       "      <td>4.046355</td>\n",
       "      <td>3.945306</td>\n",
       "      <td>4.147404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.053446</td>\n",
       "      <td>3.946924</td>\n",
       "      <td>4.159968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.745855</td>\n",
       "      <td>4.642998</td>\n",
       "      <td>4.848713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Broad</td>\n",
       "      <td>3.838248</td>\n",
       "      <td>3.734523</td>\n",
       "      <td>3.941974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Broad</td>\n",
       "      <td>4.050913</td>\n",
       "      <td>3.948047</td>\n",
       "      <td>4.153780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.023183</td>\n",
       "      <td>3.914872</td>\n",
       "      <td>4.131494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Concentrated</td>\n",
       "      <td>4.370546</td>\n",
       "      <td>4.262701</td>\n",
       "      <td>4.478391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth  Risk Concentration  Predicted    CI_low   CI_high\n",
       "0    Low   Low         Broad   3.843515  3.733141  3.953888\n",
       "1    Low  High         Broad   4.046355  3.945306  4.147404\n",
       "2    Low   Low  Concentrated   4.053446  3.946924  4.159968\n",
       "3    Low  High  Concentrated   4.745855  4.642998  4.848713\n",
       "4   High   Low         Broad   3.838248  3.734523  3.941974\n",
       "5   High  High         Broad   4.050913  3.948047  4.153780\n",
       "6   High   Low  Concentrated   4.023183  3.914872  4.131494\n",
       "7   High  High  Concentrated   4.370546  4.262701  4.478391"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pred_table_wealth(model, wealth_group):\n",
    "\n",
    "    rows = []\n",
    "\n",
    "    for b in [0, 1]:\n",
    "        for a in [0, 1]:\n",
    "\n",
    "            r = baseline.copy()\n",
    "            r[A] = a\n",
    "            r[B] = b\n",
    "            r[WEALTH] = wealth_group\n",
    "\n",
    "            newdf = pd.DataFrame([r])\n",
    "            pr = model.get_prediction(newdf).summary_frame(alpha=0.05)\n",
    "\n",
    "            rows.append({\n",
    "                \"Wealth\": wealth_group,\n",
    "                \"Risk\": \"High\" if a == 1 else \"Low\",\n",
    "                \"Concentration\": \"Concentrated\" if b == 1 else \"Broad\",\n",
    "                \"Predicted\": pr[\"mean\"].iloc[0],\n",
    "                \"CI_low\": pr[\"mean_ci_lower\"].iloc[0],\n",
    "                \"CI_high\": pr[\"mean_ci_upper\"].iloc[0]\n",
    "            })\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "wealth_low  = pred_table_wealth(m4, \"Low\")\n",
    "wealth_high = pred_table_wealth(m4, \"High\")\n",
    "\n",
    "wealth_pred_table = pd.concat([wealth_low, wealth_high], ignore_index=True)\n",
    "wealth_pred_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62549d9d-83fa-4a64-9757-45f90839661e",
   "metadata": {},
   "source": [
    "## 3. Figure 3. Amplification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "302799d8-d763-48cc-bc1e-6fccff05a701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Renter DiD: 0.4002127301041778\n",
      "Owner  DiD: 0.17186403338588851\n"
     ]
    }
   ],
   "source": [
    "def diff_in_diff(table, group_col, group_val):\n",
    "\n",
    "    sub = table[table[group_col] == group_val]\n",
    "\n",
    "    # High-Low under Concentrated\n",
    "    d1 = (\n",
    "        sub[(sub[\"Risk\"]==\"High\") & (sub[\"Concentration\"]==\"Concentrated\")][\"Predicted\"].values[0]\n",
    "        -\n",
    "        sub[(sub[\"Risk\"]==\"Low\") & (sub[\"Concentration\"]==\"Concentrated\")][\"Predicted\"].values[0]\n",
    "    )\n",
    "\n",
    "    # High-Low under Broad\n",
    "    d0 = (\n",
    "        sub[(sub[\"Risk\"]==\"High\") & (sub[\"Concentration\"]==\"Broad\")][\"Predicted\"].values[0]\n",
    "        -\n",
    "        sub[(sub[\"Risk\"]==\"Low\") & (sub[\"Concentration\"]==\"Broad\")][\"Predicted\"].values[0]\n",
    "    )\n",
    "\n",
    "    return d1 - d0\n",
    "\n",
    "print(\"Renter DiD:\", diff_in_diff(home_pred_table, \"Homeownership\", \"Renter/Other\"))\n",
    "print(\"Owner  DiD:\", diff_in_diff(home_pred_table, \"Homeownership\", \"Owner\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce0204ed-47c3-4146-b272-cbb68195822b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low wealth DiD: 0.4895691771406008\n",
      "High wealth DiD: 0.13469777948358352\n"
     ]
    }
   ],
   "source": [
    "print(\"Low wealth DiD:\", diff_in_diff(wealth_pred_table, \"Wealth\", \"Low\"))\n",
    "print(\"High wealth DiD:\", diff_in_diff(wealth_pred_table, \"Wealth\", \"High\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08963441-a092-4f97-bcf0-2bb3091ec717",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af1ef3b8-e8f2-4ab5-a084-fbfecdb77fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Amplification effect (DiD) — Homeownership ===\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>DiD</th>\n",
       "      <th>SE</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Renter/Other</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Group    DiD     SE  CI_low  CI_high\n",
       "0  Renter/Other  0.400  0.061   0.282    0.519\n",
       "1         Owner  0.172  0.059   0.057    0.287"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Amplification effect (DiD) — Wealth ===\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>DiD</th>\n",
       "      <th>SE</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low wealth</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High wealth</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Group    DiD     SE  CI_low  CI_high\n",
       "0   Low wealth  0.490  0.072   0.349     0.63\n",
       "1  High wealth  0.135  0.074  -0.010     0.28"
      ]
     },
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mio+51NY05xi7WB2M2yrltJBSf3Yojk43CtKonL5oOnuTlHaCakQ23njjos+JR75nzZrl/LpqbNTYaiQ79+A3PtuVP9qYuzPRwWf+e2lnptfT47feemu9v9XIeqED2njnVOhsoHaaekxn11qy063OnzpE6rB8/vnnUXOo06HX/81vftPo+7ocjGhnre2Fzi4++uij9jF1KgrlJXekOPbggw8WfJ+GNOUzO/zww+12nYko9HyduXQVf56uP7l0AKltBx98cL3XVeOoASM9/uqrr9ZrZDW7I39gRWdD4vfRAXE+ndXIr5NxZzX3zE2hAzH9G9PZKG07++yzC+5fNJCgTrkGFfL/Jrfe6+yADrDj+jdx4kS7XQdhOtOl73ZuhzeOXfuu/AETDTZpgECd6NzHdCBUaPAnv+Ovs+7576OBmUJnTu677z77uAZGXMQd5UJnFOKBq/yDpsY63YVm6mhAUfs9dWjyBwOQ7Ta7KfvB/E6LvgtJxHU0dzaKvofxDI7cAab8/f9vf/vbeo/FZxEb2rfldzrifYe+F4VmxMUzUjSjqCU73eoAavuxxx4bNZfOZup7nd9Ja0qnO+7Ea3ZQIZtssol9PHcANvdYq9CZ5nhQJ/9Yp5imfGY69lGbogHlYs9/5513Ildqc/PbBc260Cy9uM3RzKTcuDRgEc/+mjdvnt2mDno8yJQ/q09UpiTHFMWOsZvb6S7VZ4fiuKYbBen6Z9F1JA2ZPn26vYb45JNPtqts63oeXQ+i61gmTpxY9O90LagUu6arEN0aTG2Irk/NvU5G11Lq/p9aDbWmpqbg3+pazvxYdI1lXA7dlitfvIiZVlotRO+bb9iwYfZ9dK1Zsb8rB11PpWuj9f7xtWUu3nvvPXsrmOOOO85e96PPT9dZS0OfnytdU6sfXVNbKMe6FlfXaelaNS1Ek6/Q3+j6Y2lKfpN8Zscee6z9N/86xvh3XR+WlK4B021kiv0UEl8vFq9fkEvXXOn64Nzn5V/Plb9ATlyvtb3Q/TwL1funn37a/vuzn/2swevftNKrS7l1zaI+W60d8Nprr9Vu33777e2/8TXKur5M9xpWOeOyxo/pem6tDaFr63R9faHYc/cTov2ErsPX2ge612pz4ovtsssuBRcy23DDDe313bp+UHUm9/0aUso6X6i+6/puXWOu6821yjLCarOb2nY1pc2eP3+++ec//2m/B7nX2up7GF933tCCag21yw3t24p9T3Q9ud47X1yWQvvQcnr22Wftv1qzwZW+t1qP5cwzzzRHHXWUbbP1o5h1fKTPr7ka2nfHC+vlPi+X1m0ptMJ9U/dhST4zHfuoHuta9Nx1RtSG6LpwHQcmWWAwfzE1HWPpWnJdUx+3OfF13fFz9Liuw9c+Vuvk5LYvWhuh0CJ78TXehdqXph5jN0WpPzvUxy3DUJAWY5F4p1HI1VdfbRd00AFsUloMQ5Is6BM3zvEOP6YOnRbZ0AJruv9noYW8it0OQzs6HQwXelyPycKFC+s9poOTYrnRjn/27Nl2gauWWslbC1yJ6+qoWohGeVLjXUyxAYwk4kW+ig0EaDEPPaZ6oMZFi8Pk0iIx+eK8F/pcGpL0M9PiPbpdjg6MpkyZYjtrugeuFuJSudS4J3XkkUcWrJ+xQguqxDkstghPvF35y9dQvVauC3VWC9X7eJGd+ACjGHWCm1Pu/I61FiXSgaQWSNKCMxpk0GPKY/ycQgMHxepOsfoTx9fYbXNy44sVu12XDlQ0oKVbN2mARoM4WpRm5513tnVAi+O4lrupdb7Y906514BboUX4kN02uzltV1PabC0oqHZEA1O9evWq85jacS3yp8H0iy66qHbRL9f9V9I2O46voX1RS38fkrbbzz//vB0waWjgo5TtdlPanCT7XRdJPzPtZ7V4pwY6hw4dWmegXIMUSeR3ujVIqUGP+D7eEr+HnqM6XehWYXH7cu6559qfYvIXMmzOMXZTlPqzQ310ulFQfDuqYjtwjeadeuqp9nlqOHXgq4N4HRSLGtiGRsXiAwSd5XShEcB4dU6N8OaLV7NWx7xQp6bQ6GKSx7NGO3N1uNdbbz3bMVDnUgdjOnOgPGuF6ZZalbvQwVZr+VzUgKvD9Ne//tX84Q9/sJ1iNYAacS7UYU1DU/OXJLfxavYHHHCAXUW+GK2s2pxy60yVVg7X/kUHILkda62yrI63OuISPxafHS9FfMVmG8S22GKLettUrmK0j9TMHHU+nnnmGXtA9qc//clcc8019l/d97g55QZc2+zmStpm5w6Ua7Xr3I5KLF41Wx2WQvczLtX+KwvU2dO+RLN/tNK8Ot/qkGpF63jVbrXpLdFut+Y2+yc/+YltQ+6//357bKozwnfddZcdkGlsVf9866yzjp3hocEFDbzHHercuqyTPhpgjR8r1OmO2xf9nesAS3OPsZsi7c8uBHS6UZCmf4p28IXEt/u6+OKL6x2oakpZoanCuTSaHu+wXOROQcudjppPB+LqgBcbsSsVlV8HN4XOGGg6teSP7JeTGhlxnVoWf35qpNWw5Jo8eXLJyhXnQLcsKSZ+rLGzjGl8ZjqQOe200+wUYd2qRbdyUsOk27u0FJVJB63KU/5nleQsbXPo+6QyaNRdU9Bcy60DFZVbU5pdy61OtA6SdNCh77P+Nt5P6MBDnW5Nq3v55ZftZ+lansbi03fnqquuKji9rrnfzRNOOMH+aNrhvffeay/l0K2PdPsdl+nATaXca2Ct0PaW3kch3Ta7uW1X0jZb09010BSf0Y3P6hZr3wt1ukutWDuU1vdB+wbty7Tv0eUoDVFnTrPyNPAZ374zrXa7Jdqcpn5mGgzXGW0NTGgWhWZoqO5qdlRjs0AKDS5odpdmt2lgSD86MZE/+Krn6PZbmgmnaez5HfP4eFTTy9WRdtHcY2y0TgxroGgDrlE1NZS6t2CxBrhQ51b3Cm1otHXevHl2SpCmZboccOpAVffqjK/j/P8LANb70RlujShqR9sSNJKaT9fu6KBHZ+xaamp53BlRY6N7nzZ0cOPy+RWKS+LrY3PvG9oYfcb60XS45557rt7juiRAZdH9ofOnlreGz0xndTVlTPVVnSQdIOlShmJTisshPhgtdCmALhPQ9y33eeWg+6LmHgg0t9yqD6qr8UFNrni6uK4F1bV4mlqe/9j111/f4PXcLRFfU+jaRNUnXbqgz67U1+S51Pfx48fbqeU689PYgT6y02Y3t+3SvaTFtc7oe6+2Qp3EYm22BuVE+7CWmLqqfYquy82ngTApdDa+nDTtXv7yl780q83Wmii6d3Qh6iQmabMb23eLBkVzn9faPjN1sLWv1bTypk4tLzTFXD+6/jp/DQ+1YarPapd++OEHe+a7R48ezWpfmnOMXYpjN5QHnW40uLPRzq7QYjvxwgqadpt7vYkWsCg0/TvXuHHj7A6j2LTQfE899ZQ9SNcZm0Jn+mLx1KG4QSg3jTbHI76iTsDpp59u/18Lk7UkjfbqrJmm5GpUNH/RJp11iEdgcz8/LdCR68EHHzR33HFH0fcQnfFMQmf45De/+U2da9E0Uhvnq9A029bymcULqmk6cHMa76bSWXVNIdRBhgYpYhpgOuecc+zUN53tbex66+bQQj86qNelCDpAjKfLxdSYqxOtQbGY8qlZAddee6156623arers6k6odF6LVwWz9LI71jrYEn7idxrtuPruuODVNd9SGN09kE51qwGHeTlU4dA3434MhYXL7zwgl0PID9X6mioA6MBh3LPyLnuuuvq7L/VGVPulVedbVfMCKPNbu5+MF7kyfU7F89Oa2hKr67L1aJUuk788ccfN+WmmPPPEmtfMnbsWNtJ0qJdLUmLY+nyFO3r4hMLMX1HNVMgHoyI22ztn3LbUeVObUSx637VbusMbJJr8TWNXflQJzN/QCDen+ssd0vkqymfmQaN9tlnH/s90MworZ+x+eabN6vTrY6ujqMKdfLjtveGG26o8zcxnRlXx1uzNPX90omnfP/5z3/ssW4pjrFLdeyG0qPTjaL23HPPOtdO5tIBm0bVNe1G1//+/Oc/tzsVjYJrh9PQCtrx68WvX4rGW7RAkToGOvDXmZxy0plOrU6pQQDt3LXjHzRokHn//fftmbkTTzzRtDRd96NFPXSwr/zr2iZd96WDGnVscketddZWZwg1XVgremoatc6+aSXsU045pehKsjpAUOdzjz32sA29RpQb25Hr9fR85UY5Ukdrv/32s42KOh+acvXrX//alFtTPzMN9sSLpajO77333mUva365R48ebTtvem99v/R5adq1pkPrwKPcA026jlN3B9D1ZeqA62BZ9Uur2+p7pymnWlhOZ1xiOsjRAkk6wFAd1P5B32HNatABpnIfH6Tk0jVv6oxqAEkd09yD/Pi67njBmWKLqCWlMun7oYObuF6ojqi82223nZ1yru9GoYXUitFBlGJWnVH91xluzZJYe+217QHw8ccfX/bprHpPHfDps9E+WrnVQIC+A/psEE6b3Zz9oK4nVkdLazbEK4Q3RPv1t99+207t1X6iIS6rmJeKBkwvu+wyu6Ch9qHaR2l/prPBmh5c6C4E5aQ2UJcuaT+ntlqfifKhnKkNV5urwUlRO639iS4B0N+pDdWPFvjUTKx999234HvoM9agqNZt0f5AbfYVV1zRYLk0w0szBjUop/zExwh6DQ2Qa5aM9pcNrWeR9mcWD5Y39U4jMQ1oKw/xmedCnW4dA6uNiJ9TaAaA2midJb/xxhvtZ6vvmtpP3cFF30kdO+d2uptzjJ1PbaaOjTVwrDZTa9KoHuSu8I6WQacbDY526iD7nnvuqfeYdjA6Y62doM5cPfroo/aMmw7kGloRW/R4VVWVbTAaowN2vXax25zkUicyvn1SuRtwNZLagWmkWlNgNUqvXGllSp2NLHSLi3LT9UrqcKvzrcZb14ApdxoV185dZ8Jj6khoFHunnXay14Kp/JqCpNXfi53pUAdBHS/twPW3t912mx2FbWwxD30uKofKpQM2nRHVVEY1JOp0KY8tsYBHcz4z5SluCNP4bA855BD7eaqB1gGt4tDBmG4dooPbJAuYNZU+d+VNAzY6mNYZBC0Qpmvr1DHWAVA8XTKmM/HKsx7X/kJnaXSmWq+hmRe5U/ByxR3t3Ou5Y3FHWwd+pbieO6aDVnU8NACkuqKzTKoXOruhwQ5NyS10bXox+qx++9vf2gNkdcB1puSjjz6yB236nun7UG46K3XppZfaz0jfXcWl77fqUrzwFsJos5uzH9S0WA10uc7yidtftfGNdcxUZrURWnHa9bZ6TaXBU+231JlR3NqXatBQgxRNuRtFKei4RgMa6hBrsTrtG7RPV0dMg6rxiuyi77A+J+0Tn3zySfs8/b3Wvyi2wJ2+/xrgiy/TU5udO2OqGOVF+2wNAmiGj+qMZqepnCpvS0wtb85npvKpY64Oc7Fbn7nQa6jtixWaUabvVDwwH793PnV61cnVPlntiM7CK6dqc3RcpIEQzbQqxTF2Pn0H9Zmr064TUmqrVQ/ixYnRctroZt0t+H7wjM5SaqVd7WRLcYCrKUHaOemg+/LLLzc+im9zlTs9D9mlXaTOTqqB0plclzM9QJo0E0EdbZr38JS6zRbN0NBAq86ylnqhwZagRTA1RVlnlRu6ZSOyQx1TdVh1uZ06mUBrwJluNEj3mNVIq0ZLS0HThDQim/SaFCAtGo3WFHpN+aPDDSCkNlsLdGlmktY98LHDjfDoMqH4pE5Lr68DNIRONxqkKTFaYCVeSbg5NJ1GU2R0UKB7HwKtma550sq7mtqt6Y+FbtMCAFlts+X3v/+9fU3NTgNaMx1f6nplXTutS3q0XozWqgFaC6aXAwkxvTycz1nXN2qRIR14qgMO+IDp5cD/ML08rM9Zt6LV4pW6ewMneNCa0OkGAAAAAKBMmF4OAAAAAECZ0OkGAAAAAKBM6HQDAAAAAFAmdLoBAAAAACgTOt0AAAAAAJQJnW4AAAAAAMqETjcAAAAAAGVCpxsAAAAAgDKh0w0AAAAAQJnQ6QYAAAAAoEzodAMAAAAAUCZ0ugEAAAAAKBM63QAAAAAAlEn7cr0wymvZsmVmxowZprKy0rRp0ybt4gAAUCuKIlNTU2N69epl2rZt3vg+7R0AwPf2jk63p3QAstpqq6VdDAAAivriiy9Mnz59mvUatHcAAN/bOzrdntKIf/wBr7DCCs16rY9nzDXDR79h7j9mSzOk14olKmF2TZgwway55pppF6PVI09uyJM7cuVPnubNm2c7ynFb1Vrau4aE2Ba2hrrSkkKLN8SYiTf7JrSymF3bOzrdnoqn2OkApLkHIcvXRKZtxXJm+crmv1YIVl99dfLkgDy5IU/uyJV/eSrFdPBStncNCbEtbE11pSWEFm+IMRNv9q3eSmNurL1jITUgoVKcuQkBeXJDntyRKzfkCa5CqyuhxRtizMSbfZWexkynG0hoypQpaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/ZN8TRmOt0AAAAAAJQJnW4goXbt2qVdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZ187TmBN1ui+44AKz/PLLOz32+eef2wvKH3zwwUQFaurfpe3FF1+05X7rrbfq5OT111+v91w978orr2zhEqJUBg0alHYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfIE9jLtuZ7p49e5qxY8eanXbayYTqwgsvLNjpht+mTp1qsmLpssiM/ew788h70+2/+r1UspSnciJP7siVG/KENOpKOduTUgnxuxFazMSbfVM9jblstwyrqKgwW265ZbleHkjN/PnzTRY89eFX5sLHPjZfzV1Qu63nip3M+XsPMcPW7dns189KnsqNPLkjV27IE1q6rpS7PSmVEL8bocVMvNk339OYy3amu9A08UWLFpkTTzzRrLTSSqZr167mmGOOMffcc499np6fa8GCBeb444833bp1s2fNTzvtNLNkyZKi7/fKK6/Y1/nss89qt+29995220cffVS77eCDDzZ77rln7e8LFy4055xzjunXr58dKFh77bVtmXLpjP0+++xjevXqZbp06WI23HBDc+eddzrdq+3000+3/68fTUGPLVu2zE4/79Gjh6mqqjK/+tWvzA8//NBIVgFTsgOkUXe9U+cASb6eu8Bu1+MAADSG9gQAytTpVuc3/0edyMacddZZZvTo0ebMM8809913n/0bbSvk3HPPNW3btjX333+/OfbYY81VV11lbr311qKvvfnmm5tOnTqZl19+2f6u13711VfrbBP9/3bbbVf7+/Dhw22ZTj31VPP444+bYcOGmREjRpgnn3yyzjSGrbfe2r7/Y489Zvbff39zxBFHmL///e9Fy6OOupxwwgn2//Wz8cYb1z5+/fXXm4kTJ9rXOO+882xH/6KLLmo0h0ifBl98pil/OiNRaOJfvE2PN3dqoO95ainkyR25ckOe0FJ1paXak1IJ8bsRWszEm329PI058fRynY3t0KFDwcd0FriYWbNmmZtuusn89re/tZ1u2X333c0uu+xivvjii3rP32KLLcy1115r/3/XXXc1L7zwgj1rrg54ITpLrY63OtU6a/z+++/bsh5++OHmpZdeMqNGjTKTJk0yM2bMqO106zUfffRRM2bMGLPbbrvVvtdXX31lzj//fLPHHnvYbQcddFDt+0RRZP/+yy+/tJ31kSNHFixPPLW+b9++BafZ6+z93Xffbf9fHf133nnHxnfZZZcVfD2dkddPbN68eSZUt74y2dz6Snr36FsWLTNt2/i78P/CJUvN7B8XF31ch0Y6Y7HpH54xFe3bBZunUjhy2/7myG0HpF0MwCst0d4VakcWL/3vyYORt71pOrQLY9/V3P10S7UnpeJ7u0SbAvgrcae7c+fOdc4cx/7yl7/Um5ad64MPPrBTxjVNO9e+++5rnnvuuXrPjzvBsSFDhpjnn3++wbKpMxyXQWXcdNNNbcdZHe5423LLLWe3y9NPP22numuxt9yp6+p4q3O/dOlSuyz97NmzbSf8kUceMdOnT7fbZeWVVzZNpffIj+/ee+8t+vxLL73ULsyWb8KECXbVeE3X1zR1DSzEBg8ebMtbU1Njf9dUdg1OTJs2zf7esWNHM2DAAPPl9C/t71M+/9wM6DbIHuzMnDnTbqusrDS9e/c248ePr33dgQMHmurqajNnzhz7u95Xz4tvVq+caWVBzRCIr7uIR6U06BHXI03p19n+OJ/9+/e3ZdVrS7GYZnw7y3w9r+40NpTefw+kih9MoXGzaubb/UdD3yfNytF+KK3vk8s+YvLkyfbyoHggMa19hPbTq666aqZiKsfnpNfSzKo0Y/ruu+9MU5WzvYtzNOXL4u3Idz/8t16gdGhPSmPODwtqv8Ou3089pv1AltuG3Jj0o/fJUkwNfU4qyworrJCpmKY38jnF7Vxricm1vWsT6dStI12DrFtdff/9940+pmu0VZAHHnjAHHDAAbZDqeuplcjcaQGaZq4zyQp69dVXr/d3sZNPPtn861//qnftd65nn33WdmZ1Fvqkk06yCT7jjDNsYj/99FNz8cUX27PqcSf/qKOOanDKup7bp08fOzCgVcg1DXydddaxlVtn7VX2OF5dr73jjjuacePG1XbqdR33FVdcYa9Hr5P0AtuvueYac8opp9gz6a4j/6uttpqZO3du7ZetqT6cPtfsdd2r5vETtjHr9l7RtHZpn+lesmSxad++8GwPHzR2ZiLWbbkOzToz4XueWuqshBoFNTJoHLnyJ09qo1ZcccUmtVHlbO8aO9OtDvfKXToGc6a7ufvplmpPSsX3dqkpZ7pbw/6gJRFv9o1vZTG7tndlW7280HRq+fbbb+t0ur/55puSvcdWW21lp77rjLYWVtPUcp1BUkdZU8y1/dBDD619vh5bZZVVzBNPPFHw9bp3727Pzuta76uvvtpenx1zuYa9lDTKox+o0RmQ6vQqjYJppMtXurZum8uft4vcFBri0RKAq67Yybx65k6mXdv/LggYYp5aikZO4YZchZGnlmjvCrUj8QD03w/f3IsB6FJo7n66pdqTUgmxXfJ9f5AU8WZfZ09jbrGh3HXXXdcuaqYp2rl09rpUdE25ptTpWmud6t9mm23s9u23395eP62z6bmLqOl6cg0CaKqCzk7n/2i7RtvVwdb/xzTlQdeCN0YDAOq0I1t8b7B14KPbuEj+IVD8ux5v7gGS73lqKeTJHblyQ57QUnWlpdqTUgnxuxFazMSbff08jbnFOt26/lnXVmuK9x//+Ed7PbWmd+saLVuQtqUpijrVOqu9wQYb1J7i1zZN/1YnWGfDY5qKrtuKaSEzTe/WNeNanVyLmR155JH2OZousNlmm9ltWuhMgwT6O21vjG4/pkEGleett96qvT4BftO1HL7TfVNvGrGxPQORS79reynuq5qFPLUE8uSOXLkhT2jJutIS7UmphPjdCC1m4s2+iZ7G3GLTy0Ud18WLF9tFUnT2eL/99rO3DNP9uF06sS50VlvXS+ee0Y7/X2ev86ckxCuG33jjjXbakcqhs/JaAT2mxdl0T3GtVK7BA91rXNdy6xr2htxwww322nIt5qaL8rVa+g477FCSOJGeePEE3+lAaNchq5o3p8wy39QsMN0rO5nN+69UsjMSWclTuZEnd+TKDXlCS9eVcrcnpRLidyO0mIk3+5Z6GnOihdTK4ZBDDrH3045Xj0P5F6nxfSG1tLW2BRxaK/Lkhjy5I1f+5KmUbVQpX6shIbaFraGutKTQ4g0xZuLNvvGtLOZWt5CaaJr1a6+9ZjbZZBN7plsLlOlaay1SBvhCq+ujceTJDXlyR67ckCe4Cq2uhBZviDETb/b19zTmFr0nhu6vqY728OHD7W24dIsvdbh1OzDAF1yb74Y8uSFP7siVG/IEV6HVldDiDTFm4s2+Gk9jbtFOt85w637XOv2uG5rr3tl0uOGb6urqtIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/uqPY25RTvdAAAAAACEhE43kFDXrl3TLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Ovqacx0uoGEqqqq0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyr8jRmOt1AQpMmTUq7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizb5KnMdPpBgAAAACgTOh0AwAAAABQJnS6gYQGDx6cdhG8QJ7ckCd35MoNeYKr0OpKaPGGGDPxZt9gT2Om0w0kNH369LSL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7pnsaM51uIKGampq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+2o8jZlONwAAAAAAZUKnG0ioR48eaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/b18DRmOt1AQhUVFWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2VXgaM51uIKFp06alXQQvkCc35MkduXJDnuAqtLoSWrwhxky82TfN05jpdAMAAAAAUCZ0uoGEOnbsmHYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WZfR09jptMNJDRgwIC0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+wZ4GjOdbiChyZMnp10EL5AnN+TJHblyQ57gKrS6Elq8IcZMvNk32dOY6XQDCS1atCjtIniBPLkhT+7IlRvyBFeh1ZXQ4g0xZuLNvkWexkynGwAAAACAMqHTDSTUt2/ftIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/v6ehoznW4goYULF6ZdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZt9DTmOl0AwnNnDkz7SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb6ZnsZMpxsAAAAAgDKh0w0kVFlZmXYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WZfpacx0+kGEurdu3faRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfb09jZlON5DQ+PHj0y6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uwb72nMdLoBAAAAACgTOt0AAAAAAJQJnW4goYEDB6ZdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZN9DTmOl0AwlVV1enXQQvkCc35MkduXJDnuAqtLoSWrwhxky82Vftacx0uoGE5syZk3YRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfHE9jptMNAAAAAECZ0OkGEqqqqkq7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizr8rTmOl0AwlVVlamXQQvkCc35MkduXJDnuAqtLoSWrwhxky82Vfpacx0uoGEpkyZknYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfFE9jptMNAAAAAECZ0OkGEmrXrl3aRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfe08jZlON5DQoEGD0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uwb5GnMdLqBhKZOnZp2EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/Fm31RPY6bTDSQ0f/78tIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/vmexoznW4AAAAAAMqETjeQUK9evdIughfIkxvy5I5cuSFPcBVaXQkt3hBjJt7s6+VpzHS6AQAAAAAoEzrdQEIzZsxIuwheIE9uyJM7cuWGPMFVaHUltHhDjJl4s2+GpzHT6QYAAAAAoEzodAMJde7cOe0ieIE8uSFP7siVG/IEV6HVldDiDTFm4s2+zp7GTKcbSKhfv35pF8EL5MkNeXJHrtyQJ7gKra6EFm+IMRNv9vXzNGY63UBCEydOTLsIXiBPbsiTO3LlhjzBVWh1JbR4Q4yZeLNvoqcx0+kGElq6dGnaRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfUs9jZlONwAAAAAAZUKnG0iof//+aRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/b19zRmOt1AQjU1NWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/21XgaM51uIKHq6uq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+6o9jZlONwAAAAAAZUKnG0ioa9euaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/Z19TRmOt1AQlVVVWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2VXkaM51uIKFJkyalXQQvkCc35MkduXJDnuAqtLoSWrwhxky82TfJ05jpdAMAAAAAUCZ0ugEAAAAAKBM63UBCgwcPTrsIXiBPbsiTO3LlhjzBVWh1JbR4Q4yZeLNvsKcx0+kGEpo+fXraRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfdM9jZlON5BQTU1N2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm301nsZMpxsAAAAAgDKh0w0k1KNHj7SL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7engaM51uIKGKioq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+yo8jZlON5DQtGnT0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyb5mnMdLoBAAAAACgTOt1AQh07dky7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizr6OnMdPpBhIaMGBA2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm30DPI2ZTjeQ0OTJk9MughfIkxvy5I5cuSFPcBVaXQkt3hBjJt7sm+xpzHS6gYQWLVqUdhG8QJ7ckCd35MoNeYKr0OpKaPGGGDPxZt8iT2Om0w0AAAAAQJnQ6QYS6tu3b9pF8AJ5ckOe3JErN+QJrkKrK6HFG2LMxJt9fT2NmU43kNDChQvTLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Fvoacx0uoGEZs6cmXYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfTE9jptMNAAAAAECZ0OkGEqqsrEy7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizr9LTmOl0Awn17t077SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb7ensZMpxtIaPz48WkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2jfc0ZjrdAAAAAACUCZ1uAAAAAADKhE43kNDAgQPTLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Bvoacx0uoGEqqur0y6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyr9jRmOt1AQnPmzEm7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizb46nMdPpBgAAAACgTOh0AwlVVVWlXQQvkCc35MkduXJDnuAqtLoSWrwhxky82Vflacx0uoGEKisr0y6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyr9DRmOt1AQlOmTEm7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizb4qnMdPpBgAAAACgTOh0Awm1a9cu7SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb52nsZMpxtIaNCgQWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2DfI0ZjrdQEJTp05NuwheIE9uyJM7cuWGPMFVaHUltHhDjJl4s2+qpzHT6QYSmj9/ftpF8AJ5ckOe3JErN+QJrkKrK6HFG2LMxJt98z2NmU43AAAAAABlQqcbSKhXr15pF8EL5MkNeXJHrtyQJ7gKra6EFm+IMRNv9vXyNGY63QAAAAAAlAmdbiChGTNmpF0EL5AnN+TJHblyQ57gKrS6Elq8IcZMvNk3w9OY6XQDAAAAAFAmdLqBhDp37px2EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/FmX2dPY6bTDSTUr1+/tIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/v6eRoznW4goYkTJ6ZdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZN9HTmOl0AwktXbo07SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb6lnsZMpxsAAAAAgDKh0w0k1L9//7SL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7+nsaM51uIKGampq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+2o8jZlON5BQdXV12kXwAnlyQ57ckSs35AmuQqsrocUbYszEm33VnsZMpxsAAAAAgDKh0w0k1LVr17SL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7unoaM51uIKGqqqq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+6o8jZlON5DQpEmT0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyb5GnMdLoBAAAAACgTOt0AAAAAAJQJnW4gocGDB6ddBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZN9jTmOl0AwlNnz497SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb7pnsZMpxtIqKamJu0ieIE8uSFP7siVG/IEV6HVldDiDTFm4s2+Gk9jptMNAAAAAECZ0OkGEurRo0faRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfT08jZlON5BQRUVF2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm30VnsZMpxtIaNq0aWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2TfM0ZjrdAAAAAACUCZ1uIKGOHTumXQQvkCc35MkduXJDnuAqtLoSWrwhxky82dfR05jpdAMJDRgwIO0ieIE8uSFP7siVG/IEV6HVldDiDTFm4s2+AZ7GTKcbSGjy5MlpF8EL5MkNeXJHrtyQJ7gKra6EFm+IMRNv9k32NGY63UBCixYtSrsIXiBPbsiTO3LlhjzBVWh1JbR4Q4yZeLNvkacx0+kGAAAAAKBM6HQDCfXt2zftIniBPLkhT+7IlRvyBFeh1ZXQ4g0xZuLNvr6exkynG0ho4cKFaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/Yt9DRmOt1AQjNnzky7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizb6anMdPpBgAAAACgTOh0AwlVVlamXQQvkCc35MkduXJDnuAqtLoSWrwhxky82Vfpacx0uoGEevfunXYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WZfb09jptMNJDR+/Pi0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+8Z7GjOdbgAAAAAAyoRONwAAAAAAZUKnG0ho4MCBaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/YN9DRmOt1AQtXV1WkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2VXsaM51uIKE5c+akXQQvkCc35MkduXJDnuAqtLoSWrwhxky82TfH05jpdAMAAAAAUCZ0uoGEqqqq0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyr8jRmOt1AQpWVlWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2VXoaM51uIKEpU6akXQQvkCc35MkduXJDnuAqtLoSWrwhxky82TfF05jpdAMAAAAAUCZ0uoGE2rVrl3YRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WZfO09jptMNJDRo0KC0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+wZ5GjOdbiChqVOnpl0EL5AnN+TJHblyQ57gKrS6Elq8IcZMvNk31dOY6XQDCc2fPz/tIniBPLkhT+7IlRvyBFeh1ZXQ4g0xZuLNvvmexkynGwAAAACAMqHTDSTUq1evtIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/t6eRoznW4AAAAAAMqETjeQ0IwZM9IughfIkxvy5I5cuSFPcBVaXQkt3hBjJt7sm+FpzHS6AQAAAAAoEzrdQEKdO3dOuwheIE9uyJM7cuWGPMFVaHUltHhDjJl4s6+zpzHT6QYS6tevX9pF8AJ5ckOe3JErN+QJrkKrK6HFG2LMxJt9/TyNmU43kNDEiRPTLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Jvoacx0uoGEli5dmnYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfUk9jptMNAAAAAECZ0OkGEurfv3/aRfACeXJDntyRKzfkCa5CqyuhxRtizMSbff09jZlON5BQTU1N2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm301nsZMpxtIqLq6Ou0ieIE8uSFP7siVG/IEV6HVldDiDTFm4s2+ak9jptMNAAAAAECZ0OkGEuratWvaRfACeXJDntyRKzfkCa5CqyuhxRtizMSbfV09jZlON5BQVVVV2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm31VnsZMpxtIaNKkSWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2TfI0ZjrdAAAAAACUCZ1uAAAAAADKhE43kNDgwYPTLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Bvsacx0uoGEpk+fnnYRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WbfdE9jptMNJFRTU5N2EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/FmX42nMdPpBgAAAACgTOh0Awn16NEj7SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb4ensZMpxtIqKKiIu0ieIE8uSFP7siVG/IEV6HVldDiDTFm4s2+Ck9jptMNJDRt2rS0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+6Z5GjOdbgAAAAAAyoRON5BQx44d0y6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uzr6GnMdLqBhAYMGJB2EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/Fm3wBPY6bTDSQ0efLktIvgBfLkhjy5I1duyBNchVZXQos3xJiJN/smexoznW4goUWLFqVdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZt8jTmOl0AwAAAABQJnS6gYT69u2bdhG8QJ7ckCd35MoNeYKr0OpKaPGGGDPxZl9fT2Om0w0ktHDhwrSL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7FnoaM51uIKGZM2emXQQvkCc35MkduXJDnuAqtLoSWrwhxky82TfT05jpdAMAAAAAUCZ0uoGEKisr0y6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyr9DRmOt1AQr179067CF4gT27Ikzty5YY8wVVodSW0eEOMmXizr7enMdPpBhIaP3582kXwAnlyQ57ckSs35AmuQqsrocUbYszEm33jPY2ZTjcAAAAAAGVCpxsAAAAAgDKh0w0kNHDgwLSL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7BnoaM51uIKHq6uq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+6o9jZlON5DQnDlz0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyb42nMdLoBAAAAACgTOt1AQlVVVWkXwQvkyQ15ckeu3JAnuAqtroQWb4gxE2/2VXkaM51uIKHKysq0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+yo9jZlON5DQlClT0i6CF8iTG/Lkjly5IU9wFVpdCS3eEGMm3uyb4mnMdLoBAAAAACgTOt1AQu3atUu7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizr52nMdPpBhIaNGhQ2kXwAnlyQ57ckSs35AmuQqsrocUbYszEm32+xkynG0ho6tSpaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/ZN9TRmOt1AQvPnz0+7CF4gT27Ikzty5YY8wVVodSW0eEOMmXizb76nMdPpBgAAAACgTOh0Awn16tUr7SJ4gTy5IU/uyJUb8gRXodWV0OINMWbizb5ensZMpxsAAAAAgDKh0w0kNGPGjLSL4AXy5IY8uSNXbsgTXIVWV0KLN8SYiTf7ZngaM51uAAAAAADKhE43kFDnzp3TLoIXyJMb8uSOXLkhT3AVWl0JLd4QYybe7Ovsacx0uoGE+vXrl3YRvECe3JAnd+TKDXmCq9DqSmjxhhgz8WZfP09jptMNJDRx4sS0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+yZ6GjOdbiChpUuXpl0EL5AnN+TJHblyQ57gKrS6Elq8IcZMvNm31NOY6XQDAAAAAFAmdLqBhPr37592EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/FmX39PY6bTDSRUU1OTdhG8QJ7ckCd35MoNeYKr0OpKaPGGGDPxZl+NpzHT6QYSqq6uTrsIXiBPbsiTO3LlhjzBVWh1JbR4Q4yZeLOv2tOY6XQDAAAAAFAmdLqBhLp27Zp2EbxAntyQJ3fkyg15gqvQ6kpo8YYYM/FmX1dPY6bTDSRUVVWVdhG8QJ7ckCd35MoNeYKr0OpKaPGGGDPxZl+VpzHT6QYSmjRpUtpF8AJ5ckOe3JErN+QJrkKrK6HFG2LMxJt9kzyNmU43AAAAAABlQqcbAAAAAIAyodMNJDR48OC0i+AF8uSGPLkjV27IE1yFVldCizfEmIk3+wZ7GjOdbiCh6dOnp10EL5AnN+TJHblyQ57gKrS6Elq8IcZMvNk33dOY6XQDCdXU1KRdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZV+NpzHS6AQAAAAAoEzrdQEI9evRIuwheIE9uyJM7cuWGPMFVaHUltHhDjJl4s6+HpzHT6QYSqqioSLsIXiBPbsiTO3LlhjzBVWh1JbR4Q4yZeLOvwtOY6XQDCU2bNi3tIniBPLkhT+7IlRvyBFeh1ZXQ4g0xZuLNvmmexkynGwAAAACA1trpvuCCC8zyyy9vQvfiiy+aNm3amLfeeqtObl5//fV6z9XzrrzyyhYuIUqlY8eOaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/Z19DTm9mkXIMsuvPBCOyAxdOjQtIuCEhowYEDaRfACeXJDnhq3dFlk3pwyy3xTU2Fmfvad2bz/SqZd2zZpF6vVok7BVWh1JbR4Q4w5a/H+r/1bYLpXdqrX/mUtXhe+xkynG0ho8uTJ3n7hWxJ5ckOeGvbUh1+ZCx/72Hw1d0Http4rdjLn7z3EDFu3Z6pla62oU3AVWl0JLd4QY85SvC7tX5bideVrzC1yTfcHH3xgdt99d9OlSxez4oormgMOOKDORfBHHHGE2XbbbWt/r66uNm3btjWbbbZZ7bbvv//edOjQwTzwwAMF3+OVV16x07Y/++yz2m1777233fbRRx/Vbjv44IPNnnvuWfv7woULzTnnnGP69etnV8Nbe+21zT333FPntceOHWv22Wcf06tXLxvDhhtuaO68884GY9b7yumnn27/Xz+agh5btmyZnX6uZe+rqqrMr371K/PDDz80mkukb9GiRWkXwQvkyQ15aviAY9Rd79Q54JCv5y6w2/U46qNOwVVodSW0eEOMOSvxurZ/WYk3CV9jLnun+4svvjDbbbed+e6778xdd91lbr75ZvPOO++Y7bff3tTU1Njn6PFx48aZBQv+W7Fefvll2wF+9913a5+ja6OXLFlin1vI5ptvbjp16mT/Nu7Uvvrqq3W2xa+d+xrDhw83o0ePNqeeeqp5/PHHzbBhw8yIESPMk08+WfucqVOnmq233trceuut5rHHHjP777+/HSj4+9//XjRuddTlhBNOsP+vn4033rj28euvv95MnDjRvsZ5551nO/oXXXRRk/MMAFmbUqcR/qjAY/E2Pa7nAQCQFbR/2VT26eV/+tOfzOLFi83TTz9tVlppJbtto402MkOGDDF/+9vfbKdUnWCdcf6///s/2xlXx3i//fazf/Paa6/ZjrC2rbnmmkVviK5Oujreep7OGr///vv2zPHhhx9uXnrpJTNq1CgzadIkM2PGjNpO9wsvvGAeffRRM2bMGLPbbrvZbbvuuqv56quvzPnnn2/22GMPu+2ggw6qfZ8oiuzff/nll7azPnLkyILl2XLLLe2/ffv2rf3/XD179jR33323/X/Fp4GIBx980Fx22WUFX0/50U9s3rx5jp8ASk2fKRoXYp5ufWWyufWVKYn+RvuUNm2ml61Mvlq4ZKmZ/ePioo/rUENnADb9wzOmon27Fi1ba9fUOnXktv3NkdumP2WP9q7lhLafDi3etGJuSltYKlloU5O0fx3btfU+3rQ+45Zu88re6da075122qm2wy2DBw82G2ywgT0TrU53//79TZ8+fWyHOe50H3vssWb+/Pm2wxx3uoud5Y7p8XhquJ6/6aab2o6zOtzxtuWWW85ul3ggQOXTWfSYOt56/6VLl5p27dqZ2bNn2074I488YqZPn263y8orr9zkvOg9cmkQ4t577y36/EsvvdQuzJZvwoQJdrG2rl272mnqGljIzbPKG88W0ICFBifiqf1a/U/XRHw5/Uv7+5TPPzcDug2yBzszZ8602yorK03v3r3N+PHja1934MCB9hKAOXPm2N/1vnrelCn/3cEqZ4MGDbIzBPQZiqbmiwY9pHPnznZKv872x/lUPVBZ9drSnJh0vUc8/UQNTiljmjVrli1/lmIqx+ekgSnNOMlSTI19TjULlpiv59WdCoby+u+BSfGDE7ibOWue/Q6W4vuk2W1NVc72rqF9zjfz29W2he1rKrzY5zR3P6oyr7baapmKqbHPKb78L0sxNfQ5rbDCCnWOXVsipilfzqItbAENdczRuDk/LKj9zrVEe9cm0nBBM+i6ZN3+StdcF6Kdxo477mhuueWWOtvVkVaBn3nmGfu7pnTri/rQQw+Zbt262euwNcVbZ3+ff/55ey24XuOQQw4pWpZnn33WdmZ1sH/SSSfZ9z7jjDPszuTTTz81F198sZ3u/txzz9nnH3XUUXbKeDF6rgYD9t13Xzu9XdPA11lnHbsDu+mmm8x9991XG7eu11acmiYfd+q1Y7/iiivMaaedVud1C22/5pprzCmnnGJHb1xH/tVQzp0715anOT6cPtfsdd2r5vETtjHr9l6xWa8VAn1B1SChYSHmqSmj+0uWLDbt23coW5myOtIf67ZcB850l6hOlXLUX22U2u2mtFHlbO8aEmJbGNp+OrR404o5zTPdWWhTk7R/7cwy7+NN6zMuVZvn2t6V/Uy3ziR/88039barg63p4rlnqX/zm9/Yzqs6ydpBaHr4mWeeaaeBqwHOXWytkK222soutqYz2jrDrqnlen91lHXGXNsPPfTQOmVbZZVVzBNPPFHw9bp3726vM9e13ldffbU9Kx+Lz+C1FI0w6gdA66Wdd9IdeIgHgS50rdo2lz9vF40pNBSpc1WrrtjJvHrmTtw+LGN1ivYOCK8tLBXf939J27+JEz71Pt5QPuOyL6S2zTbb2DPLmqId01lnXXOtx3I73epkq3MbTyPXKuE6la/rnDXKvfrqqzf4XlpZXIuV6VprneqPX19T1nX9tKbk5E5R32WXXcy3335rp8no7HT+j7ars68Odu6N2DXdRteCN0YDAPHicMgOTe9C48iTG/JUmDrSui2K5Hep49/1OB3u+qhTcBVaXQkt3hBjzkK8Sdq/LMSblK8xl+RMt6aJaxp4Pi1spinTt99+u12o7Nxzz7Wd0N/+9rf22o/DDjus9rkasdCZZZ2Rvvbaa2vn12vVcE0z/+Uvf+lUFnWqNXVbne/4FL+23XDDDbYTrLPhMU1F123FNNVd09DXX3992/HX1HZd26Kp55ouoFuXqeOvs+Lt27e3/6/thc7g59Ltx3QduM7Qa0BgrbXW8rai4H903RUaR57ckKfidB/Sm0ZsXO8+pRrh5z7dxVGn4Cq0uhJavCHGnJV4Xdu/rMSbhK8xl+RMtzrSBx54YL0fTefWGWp1pHWdtjrORx99tF1ETdPI8zug8Vno3LPROkudv60hhZ4f/7/OXuvMeS4NFmjRtBtvvNEuuqZbgWmBtfh1RIuz6fpwrVR+4okn2vuM505TL0YdfZ0l1+uq4/722287xYDWLXehExRHntyQp4bpwEJT6P5x1JbmzO2623/1Ox3u4qhTcBVaXQkt3hBjzlK8ue3fnw/asGD7l6V4Xfkac7MXUkM6mrNITb4QF48J8VqSlkae3JAnd+TKnzyVso0q5Ws1JMS2sDXUlZYUWrwhxky82Te+lcXs2kaV/ZpuAAAAAABCRacbSEiXGqBx5MkNeXJHrtyQJ7gKra6EFm+IMRNv9g30NGY63UBC1dXVaRfBC+TJDXlyR67ckCe4Cq2uhBZviDETb/ZVexoznW4goTlz5qRdBC+QJzfkyR25ckOe4Cq0uhJavCHGTLzZN8fTmEtyyzC0vHj9O12831zf18wzyxb+aP+dN4973jbm+++/L0nes448uSFP7siVP3mK378Ua7WWsr1rSIhtYWuoKy0ptHhDjJl4s+/7Vhaza3vH6uWe+vLLL+3t2AAAaK2++OIL06dPn2a9Bu0dAMD39o5Ot6d0/+8ZM2bYe523adOm2SM0OqBRZSnn7ViygFy5IU9uyJM7cuVXnnRoUVNTY3r16mXatm3bato7H3LXUog3+0KLmXizb14rjNm1vWN6uaf0oTb37EE+Vd7WUoFbO3Llhjy5IU/uyJU/edJ9S1tre9fac9eSiDf7QouZeLNvhVYWs0t7x0JqAAAAAACUCZ1uAAAAAADKhE43TEVFhTn//PPtv2gYuXJDntyQJ3fkyg15arrQcke82RdazMSbfRUex8xCagAAAAAAlAlnugEAAAAAKBM63QAAAAAAlAmd7owbP3682XXXXU2XLl3Mqquuas444wyzaNGiRv9OVx1cdtllpm/fvqZz585mq622Mm+88YbJsqbm6sYbbzR77bWXWWWVVew9ZB988EGTZU3J01dffWWft+GGG9p77er2P7/4xS/M1KlTTVY1tT6NGDHCDBo0yP5dt27dzHbbbWeefvppk2VNzVWua665xn7/9F3MqqbmafXVV7e5yf9ZsGCBCUVobWFo7VmI7VJobUxo7URo+/vxzfh8p0+fbkaOHGn3W9pPr7322ubuu+82rQ336c6w2bNnm5122snuXP/5z3/aSvmb3/zG/Pjjj+b6669v8G8vv/xyu1CBDjbWX399c8MNN5jddtvNvPfee2bAgAEma5qTqzvuuMP++5Of/KT2/7OqqXl6++237fMPP/xws+WWW5rq6mpz0UUXmc0339x8+OGHdkeZJc2pT2pk9Fz9rRrJv/71r7ZuvfDCC2bbbbc1WdOcXMW+/vprc+GFF5ru3bubrGpung444ABz6qmn1tnm40I0TRFaWxhaexZiuxRaGxNaOxHa/r458WrwTIOha621lvnLX/5i79390UcfmYULF5pWRwupIZsuueSSqEuXLtF3331Xu2306NFRu3btounTpxf9u/nz50crrLBCdPbZZ9duW7hwYdSvX79o1KhRURY1NVeydOlS+++UKVO0KGH0wAMPRFnV1DzNnj07Wrx4cZ1tX3zxRdSmTZvoyiuvjLKmOfUp35IlS6LVVlstOuqoo6IsKkWuDjnkkOjQQw+Ntt9++2jPPfeMsqg5edK++7jjjotCFVpbGFp7FmK7FFobE1o7Edr+/pJmxDtixIho6NChth63dkwvz7Ann3zS7LLLLmallVaq3TZ8+HCzbNmyBqcSvf7662bevHn2ubGOHTuan/3sZ+aJJ54wWdTUXEnbtuF8jZqap65du5r27etOrNFUPp1JmDFjhsma5tSnfO3atbP5SzqNLpRcvfrqq+Zf//qXPROZZaWsU6EJrS0MrT0LsV0KrY0JrZ0IbX//ZBPj1f75/vvvN7/+9a9tPW7t/Nu7ItH1EYMHD66zTTvWnj172sca+jvJ/1tdIzFt2jQzf/58kzVNzVVoSpmnCRMmmG+++cbWq6xpbp50HemSJUvMd999Z6688kozceJEc8wxx5gsak6uli5dao4//nhz7rnn2udnWXPrlK5v0/TC5Zdf3k4l/eCDD0woQmsLQ2vPQmyXQmtjQmsnQtvfj29ivO+8844dLOrQoYPZfvvt7b+6HvzMM880ixcvNq0N13RnmK6RUKXNp4UzZs2a1eDf6cvaqVOnen+nHbUe10IFWdLUXIWmVHlSPTrxxBNNr169zMEHH2yyprl50jV2Rx11lP1/NZr33XefvWYpi5qTKy369MMPP5hTTjnFZF1z8rTPPvuYLbbYwi4GNnnyZHPxxRebbbbZxrz77rut9rrkUgqtLQytPQuxXQqtjQmtnQhtfz+7ifHqOn058sgjbX2+4IILzJtvvmnOO+88O2vn0ksvNa0JnW4ALU47xueee8489dRTdqVK1PXTn/7UrqirhX0eeOABO83q4YcfNnvssUfaRWs1dDZKDasWe9KUXxR37bXX1v6/FkrSQmA6q6AzXDogBRBWuxRKGxNiOxHS/n7ZsmX2X01Nv+qqq+z/77jjjqampsbGq8++NQ2M0unOMI0QzZ07t+CIUu51E4X+Tqv+aVXL3BF+/Z1uO6DHs6apuQpNKfJ0yy23mN///vd2pH3nnXc2WdTcPFVVVdkfGTZsmB3pPf300zN3QNScXKkx1WrSOqiYM2eO3abpkvrR7zp7k3+9ps9KuY/SlD2d+dDqzSEIrS0MrT0LsV0KrY0JrZ0IbX/frRn7aNHK57n0HdYZ/kmTJpn11lvPtBatr6ahZDSylX8thCq1ltfPv3Yi/+/k008/NRtssEHtdr1WfK/SrGlqrkLT3DxpJH3UqFH24Ea3acmqUtenTTbZxC40kkVNzZX+5uWXXy7Y8dE25UsHk1nBPqrpQmsLQ6srIbZLobUxobUTfIeNU7xDhgxp8HVb273JWUgtwzRi+eyzz9aO7ommEek6B003KWbo0KH2Pnd6bkwLEujeeVqQIYuamqvQNCdPL774or1OTtfd/O53vzNZVur6pJVXW+u1WGnl6pprrrH3lc39UcdI99vV/+teu1lSyjqllZlVpzbbbDMTgtDawtDasxDbpdDamNDaidD293s0Md5+/frZM9n621zPPPOMHRRtrFPe4tK+ZxnKZ9asWVHPnj3tPQnHjBkT3XbbbVHXrl3r3b9vp512itZYY4062y699NKooqIiuuaaa6Lnnnsu2n///aPKysros88+i7KoObkaN26cvZfpjTfeaO9reuqpp9rfX3zxxShrmpqnjz/+OFpxxRWjddddN3rttdeisWPH1v5MmjQpypqm5unxxx+Phg8fHt1xxx3RCy+8ED300EP2u6d69Y9//CPKouZ89/L5cP/Vls7TPffcE/3iF7+I7rrrruj555+Pbr31Vvt4t27dosmTJ0chCK0tDK09C7FdCq2NCa2dCG1/P6sZn++jjz4atWnTJjrppJOip59+Orr44oujDh06ROeee27U2tDpzjg1KjvvvHPUuXPnqHv37tFpp50WLVy4sM5zVMn79etXZ9uyZcvszer79OljDzi22GKL6PXXX4+yrKm5GjlypG2w8n/03CxqSp5uv/32gjnSj/KXRU3J0yeffBLtu+++Ua9evaKOHTvaf4cNG9aqD3jT/O75eDDV0nlSB2KHHXaIqqqqovbt29t/ddA9fvz4KCShtYWhtWchtkuhtTGhtROh7e8/bsbne++990brrLOOrdN6XPts7btbmzb6T9pn2wEAAAAAyCKu6QYAAAAAoEzodAMAAAAAUCZ0ugEAAAAAKBM63QAAAAAAlAmdbgAAAAAAyoRONwAAAAAAZUKnGwAAAACAMqHTDQAAAABAmdDpBgL34osvmjZt2th/Y4cddphZffXV6zzv+++/N0ceeaRZddVV7fNPPvlk8/nnn9v//9vf/tbi5d5hhx3sT2tWKGcyc+ZMc8ABB5iVV17Zbr/mmmtMa/Dmm2+ajh07mqlTpzbrdVR3VIea4+abbzZ9+/Y1CxcubNbrAECM9q58aO+ajvYuDHS6gRTceOONtvHZYostjC8uueQSe7AxatQoc+edd5pDDjmk7O/58ccfmwsuuMAe7PioWM5OOeUUM2bMGHP22Wfb7cOGDSvLe//rX/9K9DfnnnuuOfjgg02/fv1qt+lAT3VVP23btjUrrLCCWWuttWwszzzzTKID3finoqLC9OjRw762yvntt9/W+xsdxCxatMiMHj06UQwAWhfaOze0d817b9o7tHoRgBY3dOjQaPXVV4/0FZw4cWKqZXnhhRdsOfRvbNGiRdGCBQvqPG+LLbaItt566zrbli1bFs2fPz9asmRJWcr2wAMP1CtbbOHChfanNSuUM+nRo0f0y1/+sqzv3aVLl2jkyJHOz3/33Xdtrl9//fU627fffvuoT58+0Z133ml/br755ui0006LBgwYYJ8/fPhwW19yqe7kbovr2Iknnmhf429/+1t0xRVXRPvtt1/Uvn37aOWVV46ee+65emU644wzon79+tl6BsBPtHduaO+ajvYOPmifdqcfCM2UKVPM66+/bv75z3+aY445xtx9993m/PPPN61Jhw4d6m375ptvzJAhQ+ps0yhup06dTBo0Lay1K5SzeHvXrl1Na3L77bfb6W1bbrllvcdWXHFFM2LEiDrbLrvsMnPiiSfas1iaXnf55ZfXPqaR/UK23XZbO80w13/+8x+z2267mf3339+e6enZs2ftY8OHDzd//OMfzQsvvGB22mmnEkQJoCXR3pUG7V1p0d4hFWn3+oHQXHTRRVG3bt3sqPWoUaOiQYMG1XvOlClT7EipRkevv/76qH///lHnzp2jXXfdNZo2bZodCf39738f9e7dO+rUqVO0zz77RN99912d19CI6Z577hmNGTMm2mCDDaKKiopo7bXXjh566KFGR/41Yqy/z308/0dljMt5++2313nNTz75JDrwwAOjqqoqW74111wzOuecc2of//zzz23s2q7HV1pppeiAAw6wrxfTaxZ637icGpHWT66ZM2dGhx9+eNS9e3cb7/rrr29HmYvldvTo0XYEu2PHjtGmm24avfnmm06f4ezZs6OTTjrJjojrb9dYY43osssui5YuXdpgzorF5Pq6Mf1+zTXXROuuu66NU3nefffdo3HjxtnHC71HY2cB+vbtGx122GH1tivH66yzTsG/0RmfIUOGRMstt1w0Z86c2u2qO7nvF+dDZ3IKueeee+zjuXUkprqhMwYA/EN7R3tHe1cX7V24ONMNtDCN9P/sZz+zI9e6nuimm24y48aNM5tttlnB5+o6nxNOOMHMmjXLjoJqNFSjoLpu6MwzzzSTJk0y1113nTnttNPMbbfdVufvJ06caH7+85+bY4891owcOdKO7h544IHmqaeeMrvuuqtTeddee217HZauy+rTp4859dRT7fZVVlml4LVJ77//vh3h1dmDo48+2o4Kf/bZZ+axxx4zF198sX2O4tXZj4MOOsi+pq5hUx50zZNGf5dbbjmz3Xbb2ZHla6+91pxzzjm2HHF5Cpk/f779e+Xj+OOPN/379zcPPPCAvVZqzpw55qSTTqrz/HvuucfU1NTYsy86g6Hc6nOZPHlywTMfsR9//NFsv/32Zvr06fZvNVquWHS92ldffWUXiSmWs4022qj2Wjfl/9BDD030urEjjjjCXju3xx572IVrlixZYl555RXzxhtvmE033dS+h7Zvvvnm9jOQNdZYo2hMes9p06aZjTfe2CTRrl07W4d/97vfmVdffdXsueeepil0NkAxPf3007V1JKYyvfbaa016XQDpor2jvaO9q4v2LmBp9/qBkLz11lt2hPOZZ56xv2sEX6O8Gu0tNDq9yiqr1BlRPfvss+12jeQvXry4dvvBBx9sR4pzr0vT6KuemzvSP3fu3Khnz57RRhtt5Dzyn38moVA5c0f+t9tuu6iysjKaOnVqnefmXqf0448/1svN2LFj7WvdcccdTte45Y/8ayRcz73rrrtqt+k6q6222ipafvnlo3nz5tUps66rmjVrVu1zH3nkEbv9scceixo7c6PrxyZMmFBn+1lnnRW1a9fOnplpKGei9znuuOOa9LrPP/987fVi+XJznOQat2effbZo7A2N/MvDDz9s//bPf/5zk0f+RXVaZ8TyHX300fasFwC/0N79F+0d7V0+2rswsXo50II0kq9VLHfccUf7u0acNTJ/7733mqVLl9Z7vkbpdX1RLF79VdcbtW/fvs52nSHQCG6uXr16mf3226/2d63EqdHmd99913z99dclj09nAl5++WVz+OGH25HrXIo11rlz59r/X7x4sfnuu+/MwIED7XVf77zzTpPe+4knnrC3KtFIdEwj+Dp7oFuZvPTSS3Wer7x369at9nedrRCN/DdEZxP0XP1tdXV17c8uu+xiP0PF3xSur/vQQw/ZXBa6LjI3x0ko/5KbD1fLL7+8/VdnUZpDr1PoNVQmndXRmREA/qC9+y/au6a/Lu0dsoTp5UALUUOigw0dgGhxmdwDiKuuuso899xzdoGNXPkNeXxAstpqqxXcPnv27Drb1bDnN0xrrrmm/VdT3NRol1LcgK+77roNPk+NyqWXXmqn/+nA6b+D4f81d+7cJr237rU5aNAge5uPXPH0vPx7cebnNm6A83OYT1MYNaVQ0w0L0aIxTeH6upq6qIPLlVZayZRa7ufgSgd4UllZ2az31usUeo24TE09wALQ8mjv/of2rumvS3uHLKHTDbSQ559/3l6rpAMR/RQ6K5B/EKJriAoptr0pjUgadM2eDkBOPvlks9VWW9mDKDUyuuZt2bJlLVKGpuZQ5dP1aWeccUbBx+ODvKTK9bouVl55ZacDsEI+/PDD2gPeptLZnwkTJhQ8eFWZdM1j7tkiAK0b7d3/0N613Ou6oL1DWuh0Ay1EBxndu3c3N9xwQ73HdDuVhx9+2Nx8880l3dlqkRU1qrmjptrZixZ8KbUBAwbUaZiKefDBB+1CNzrjEVuwYIFdACZXktHefv362ZFzNea5o//jx4+vfbwUtECLRqk1Da6UXF9XzxszZoxdaKih0f8kuRs8eLD9N/eMlOvZLC3Qo4OEbbbZxjSV6oPOBu2+++71HlOZii0mBKB1or37H9q7pr8u7R2yhGu6gRagHawONPbaay+7cmX+j1Yf1fU9jz76aEnfd8aMGfbgJjZv3jxzxx13mA033LDkU+1EU8W0CqtWldXqoMVG1DXqnj/CrhVp86/z69Kli/03/+CkkJ/85Cf2ur377ruvdptWOdXr6voprZRaClpNd+zYsfZAIJ/Kqfcs5+vq/p7K3YUXXljvebk5Ve5c8ia9e/e2Uzjfeust5/Lqs9L1g5988on9V9dPNoXuW6ozQJrueNxxx9V7XNc8Dh06tEmvDaDl0d7R3pXqdWnvkCWc6QZagA4udJCxzz77FHx8yy23tA24zg5owZNS0RQt3ZpCtyzRgjY6OJg5c6ad6lYuuuWJRoF16wvdvkO3MtH1dP/+97/Ne++9Z5+jgzHd5kPT7IYMGWIb32effbZ22ldMB0s6YLn88svttW8VFRX29jE6g5JP7zV69Gh7y5S3337bntnQiLJuv6HbjzT3GqzY6aefbj9PxaD32mSTTcwPP/xgPvjgA/t+irWqqqpsr6trJHULFuVZ18UNGzbMnu3QLVT0mA5oRX+vnF599dX2mjh9DvHCRIXsu+++9oA1/0yRKPd33XWX/X8t8KIzSjqo1vV2miJ50UUXOcWoMuoMjw5gtJiNPhvFrHqg984/MNbnqDMcKhsAP9De0d6V6nVp75ApaS+fDoRg7733jjp16hT98MMPRZ9z2GGHRR06dIiqq6trb/NxxRVX1HlOsVtR6BYm2j5u3Lh6t+8YM2ZMtP7660cVFRXR4MGD6/1tqW+hIh9++GG03377RV27drVxr7XWWtHvfve72sdnz54d/epXv4qqqqrs7U123333aPz48fVuvSG33HJLNGDAAHsbkdxy5t9CRWbOnFn7urqlzHrrrVevbMVyK9p+/vnnR42pqamxt7MZOHCgfR+939ChQ6Mrr7zS3raloZwVu4VKktddsmSJLb8+Tz1Pt9rZY489orfffrv2Ocqnbmej24/o/Rq7nco777xjn/fKK6/U2a4ca3v8o89r0KBB0YgRI6Knn3664GsVu4VK/KN6rjKrfBdffHH0zTffFHydM888M+rbt2+dW8MAaN1o72jv8t+H9o72DlHURv9Ju+MPoPQ08q2FOh5//PG0iwJP7LzzzvYsgc7KpG3hwoW2Dp911lnmpJNOSrs4AFox2jskRXuHlsY13QAA65JLLrHXCObfbiYNmhKq+84ee+yxaRcFAJAxtHdoaZzpBjKKkX8AQAho7wC0dpzpBgAAAACgTDjTDQAAAABAmXCmGwAAAACAMqHTDQAAAABAmdDpBgAAAACgTOh0AwAAAABQJnS6AQAAAAAoEzrdAAAAAACUCZ1uAAAAAADKhE43AAAAAABlQqcbAAAAAABTHv8Pc16lvlkuJMAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CAT = pb.C\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Load + Fit (필요 시)\n",
    "# -----------------------------\n",
    "# df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "A  = \"exposure\"\n",
    "B  = \"beneficiary\"\n",
    "DV = \"capital_tax_index\"\n",
    "HOME = \"homeowner\"\n",
    "WEALTH = \"wealth_tertile\"\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_household_income_band_million_krw)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(employment_status)\",\n",
    "    \"CAT(occupation_group)\",\n",
    "    \"CAT(union_membership)\",\n",
    "    \"ideology_0_10\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "# Model 3: A*B*homeowner + controls\n",
    "f3 = f\"{DV} ~ {A}*{B}*{HOME} + {CONTROLS}\"\n",
    "m3 = fit_ols(f3, df)\n",
    "\n",
    "# Model 4: A*B*C(wealth_tertile) + controls\n",
    "f4 = f\"{DV} ~ {A}*{B}*CAT({WEALTH}) + {CONTROLS}\"\n",
    "m4 = fit_ols(f4, df)\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Baseline row (controls 고정)\n",
    "# -----------------------------\n",
    "def mode_or_first(s):\n",
    "    s = s.dropna()\n",
    "    if len(s) == 0:\n",
    "        return np.nan\n",
    "    return s.mode().iloc[0] if not s.mode().empty else s.iloc[0]\n",
    "\n",
    "baseline = {}\n",
    "baseline[\"age\"] = int(round(pd.to_numeric(df[\"age\"], errors=\"coerce\").mean()))\n",
    "baseline[\"ideology_0_10\"] = int(round(pd.to_numeric(df[\"ideology_0_10\"], errors=\"coerce\").median()))\n",
    "\n",
    "for c in [\"gender\",\"region\",\"education\",\"monthly_household_income_band_million_krw\",\n",
    "          \"marital_status\",\"employment_status\",\"occupation_group\",\"union_membership\"]:\n",
    "    baseline[c] = mode_or_first(df[c].astype(str))\n",
    "\n",
    "baseline[WEALTH] = \"Middle\" if \"Middle\" in df[WEALTH].astype(str).unique() else mode_or_first(df[WEALTH].astype(str))\n",
    "\n",
    "# -----------------------------\n",
    "# 2) DiD 계산 (델타메소드)\n",
    "#   DiD = (High-Low)_Conc - (High-Low)_Broad\n",
    "# -----------------------------\n",
    "def build_row(base, overrides):\n",
    "    r = base.copy()\n",
    "    r.update(overrides)\n",
    "    return r\n",
    "\n",
    "def did_effect(model, base_row, fixed_overrides):\n",
    "    \"\"\"\n",
    "    fixed_overrides: group-specific overrides (e.g., {HOME:0} or {WEALTH:\"Low\"})\n",
    "    returns: (did, se, ci_low, ci_high)\n",
    "    \"\"\"\n",
    "    # Four cells:\n",
    "    # (A,B) = (0,0)=Low risk Broad, (1,0)=High risk Broad, (0,1)=Low risk Conc, (1,1)=High risk Conc\n",
    "    rows = {\n",
    "        \"LB\": build_row(base_row, {**fixed_overrides, A:0, B:0}),\n",
    "        \"HB\": build_row(base_row, {**fixed_overrides, A:1, B:0}),\n",
    "        \"LC\": build_row(base_row, {**fixed_overrides, A:0, B:1}),\n",
    "        \"HC\": build_row(base_row, {**fixed_overrides, A:1, B:1}),\n",
    "    }\n",
    "    newdf = pd.DataFrame([rows[\"LB\"], rows[\"HB\"], rows[\"LC\"], rows[\"HC\"]])\n",
    "\n",
    "    # Build design matrix using the model's formula\n",
    "    # (robust cov already in model.cov_params())\n",
    "    exog = patsy.dmatrix(model.model.data.design_info, newdf, return_type=\"dataframe\")\n",
    "    V = model.cov_params()\n",
    "\n",
    "    # Contrast vector: (HC - LC) - (HB - LB)\n",
    "    c = (exog.iloc[3].values - exog.iloc[2].values) - (exog.iloc[1].values - exog.iloc[0].values)\n",
    "\n",
    "    did = float(np.dot(c, model.params.values))\n",
    "    se  = float(np.sqrt(np.dot(c, np.dot(V, c))))\n",
    "    ci_low, ci_high = did - 1.96*se, did + 1.96*se\n",
    "    return did, se, ci_low, ci_high\n",
    "\n",
    "# -----------------------------\n",
    "# 3) Compute group-specific DiD\n",
    "# -----------------------------\n",
    "# Homeownership groups (Model 3)\n",
    "did_renter = did_effect(m3, baseline, {HOME:0})\n",
    "did_owner  = did_effect(m3, baseline, {HOME:1})\n",
    "\n",
    "home_df = pd.DataFrame([\n",
    "    {\"Group\":\"Renter/Other\", \"DiD\":did_renter[0], \"SE\":did_renter[1], \"CI_low\":did_renter[2], \"CI_high\":did_renter[3]},\n",
    "    {\"Group\":\"Owner\",        \"DiD\":did_owner[0],  \"SE\":did_owner[1],  \"CI_low\":did_owner[2],  \"CI_high\":did_owner[3]},\n",
    "])\n",
    "\n",
    "# Wealth groups (Model 4): Low vs High\n",
    "did_w_low  = did_effect(m4, baseline, {WEALTH:\"Low\"})\n",
    "did_w_high = did_effect(m4, baseline, {WEALTH:\"High\"})\n",
    "\n",
    "wealth_df = pd.DataFrame([\n",
    "    {\"Group\":\"Low wealth\",  \"DiD\":did_w_low[0],  \"SE\":did_w_low[1],  \"CI_low\":did_w_low[2],  \"CI_high\":did_w_low[3]},\n",
    "    {\"Group\":\"High wealth\", \"DiD\":did_w_high[0], \"SE\":did_w_high[1], \"CI_low\":did_w_high[2], \"CI_high\":did_w_high[3]},\n",
    "])\n",
    "\n",
    "print(\"=== Amplification effect (DiD) — Homeownership ===\")\n",
    "display(home_df.round(3))\n",
    "print(\"\\n=== Amplification effect (DiD) — Wealth ===\")\n",
    "display(wealth_df.round(3))\n",
    "\n",
    "# -----------------------------\n",
    "# 4) Plot: Figure 3 (Dot plot with 95% CI)\n",
    "# -----------------------------\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3.8), sharey=True)\n",
    "\n",
    "def dot_ci(ax, d, title):\n",
    "    y = np.arange(len(d))\n",
    "    ax.hlines(y, d[\"CI_low\"], d[\"CI_high\"], linewidth=2)\n",
    "    ax.plot(d[\"DiD\"], y, marker=\"o\", linestyle=\"None\")\n",
    "    ax.axvline(0, linewidth=1)  # zero reference line\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(d[\"Group\"], fontsize=11)\n",
    "    ax.set_title(title, pad=14, fontsize=15)\n",
    "    ax.grid(True, linestyle=\"--\", linewidth=0.6, alpha=0.6)\n",
    "    ax.tick_params(axis=\"x\", labelsize=11)\n",
    "\n",
    "dot_ci(axes[0], home_df, \"(a) Amplification by Homeownership\")\n",
    "dot_ci(axes[1], wealth_df, \"(b) Amplification by Wealth\")\n",
    "\n",
    "axes[0].set_xlabel(\"Amplification effect (DiD)\", fontsize=12)\n",
    "axes[1].set_xlabel(\"Amplification effect (DiD)\", fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save high-resolution PNG\n",
    "output_path = r\"E:\\\\Figure_2.png\"\n",
    "plt.savefig(output_path, dpi=600, bbox_inches=\"tight\", format=\"png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66f01a5a-5bb2-4c42-8279-e2583f8e69ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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